the generational differences essay

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The Changing Generational Values

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Examining Workplace Values from Baby Boomers to Generation Z

Today’s workforce consists of 4 generations: (ordered from oldest to youngest) Baby Boomers, Generation X, Millennials, and Generation Z. These generations were raised in different social and political atmospheres and therefore, correspond to different childhood upbringings and familial environments, which beget different values, wants, and needs in adulthood.  

 Early and late psychological researchers have proven this to be true: the environment in which an individual is brought up in, namely the things that they lack or are deprived of in their childhood, strongly influences their value development throughout adulthood.   

But how exactly does this tie into the ever-changing workplace status quo and where do employers fall in? We can generalize these individual upbringings that influence different adulthood values to the changing social, political, and technological atmospheres surrounding each generation that underlie (and influence) different generational workplace values.   

To cultivate a workplace environment where all employees can thrive, employers must be wary of these values, as well the nature of the social, political, and technological atmospheres that generated them.   

Baby Boomers

(Born 1946 to 1964) 

The Baby Boomers, or “Boomers,” were born and raised in post-WWII (post-World War II) American society. This period saw younger marriages, higher childbirth rates, and, resultingly, greater resource scarcity. Being raised in a society with limited resources, limited jobs, and limited schooling inspired a generation of competitors: individuals who operated with a “ work as hard as you can, then work even harder the next time ” mindset.  

According to liveaboutdotcom, some common workplace and worker values/mindsets associated with the Boomer generation are work-centric and workaholic, independent and self-assertive, goal-oriented and career-focused, competitive, and self-actualized. Together, these values and mindsets suggest a generation that prioritizes efficiency and efficacy in the workplace but has little regard for a work-life balance, with work tending to be the center of their lives. 

Generation X

(Born 1965 to 1980) 

Generation X, or Gen Xers, is the generation that follows the Baby Boomers. Knowing that the preceding generation was characterized by a work-centric lifestyle, it’s no surprise that the generation that followed almost entirely rejected this belief. Gen Xers were raised in a time characterized by early technological developments (analog to digital), transformative socio-political change, and minimal adult supervision.  

Together, this fostered a generation with hyper-independence (with often both parents always working) and hyper-flexibility (from having to constantly adapt to the rapidly evolving status-quo) that, contrasting Boomers, prioritizes a work-life balance: operating under a “ work hard, play hard” mentality. According to indeed, some common workplace values to Gen Xers are independence and self-sufficiency, healthy work-life balancing, flexibility and informality, and technological creativity. 

Generation Y

(Born 1981 to 1996) 

Generation Y, or more commonly known as Millennials, follow Generation X and precede Generation Z. Millennials are the most populated generation and compose the majority of today’s workforce, (approximately 35% according to U.S (United States). Bureau of Labor Statistics) which make their upbringing and workplace values especially of interest to employers. As the name suggests, most Millennials were raised at the turn of the millennium, serving as the last generation to see life before and after the complete digital takeover. In addition to witnessing extreme technological growth and development that spawned unprecedented levels of communication, Millennials were old enough to understand 9/11 and its aftermath and grew up seeing the importance and benefit to the work-life balance that Gen Xers prioritized.  

These childhood environments resulted in a highly progressive, empathetic generation that was the first to integrate moral values into the workplace: striving to only work in environments that aligned with their core socio-political values, even at the cost of a pay-cut. The Millennial workplace mindset is best described as “ work hard, play harder, but try to only work where you can see yourself play” . According to Haillo and indeed, some common workplace values essential to the average Millennial worker are personalized and frequent internal communication, diversity and inclusion, flexibility + remote options, teamwork, professional growth, and professional development (emphasis on learning new skills.)  

Generation Z

(Born 1997 to 2010) 

Lastly, but surely not least, is Generation Z, (or Gen Z’ers,) the incoming generation of today’s workforce. Currently, Gen Z accounts for 30% of the world’s population and is projected to compose about 30% of the workforce population in less than 5 years. As the first generation to truly exist without knowledge of what it’s like to grow up without digital technology, it’s no surprise that there are many qualities unique to Gen Z that clearly set them apart from the past three generations we’ve discussed.  

Growing up with emergence and proliferation of social media apps and the world wide web, Gen Z has been named “the first global generation,” with access to everything ( and everyone ) at just one click of a button. Pair this with the global economic and health upheavals caused by the global financial crisis that spanned 2007 to 2009, the global distress caused by the climate emergency, and the economic fallout from COVID-19 that transitioned the world online, we would expect this lack of stability would produce a generation of similar values and beliefs to those of the Boomers. However, what Gen Z had that Boomers did not was the ability to communicate openly and honestly about their thoughts, feelings, and experiences with tens, hundreds, thousands, even millions of other people experiencing similar (or worse) upheavals. Gen Z is the first generation to have access to every perspective; the first generation where almost no traumatic or unpleasant experience was isolated , unrelatable , or unique–  the first generation of global community.  

According to Zurich and McKinsey & Company , Gen Z is the generation of truth, exploration, and identity (or lack thereof). Gen Z is driven by an insatiable hunger for underlying truths and seeks freedom from any confining labels that limits any exploration of these truths. Resultantly, retaining Gen Z in the workplace presents even greater difficulty than retaining Millennials. Taking the previous generation’s prioritization of working at companies with similar socio-political values a step further, Gen Z has no problem leaving a company or business that contrasts with their beliefs. Moreover, Gen Z is the generation with the least regard for salary, often placing workplace values over competitive pay. For this generation, these values include meaningful work, diverse and i nclusive company culture, mental health prioritization, open and honest communication, stability and balance, professional growth and development, collaboration, autonomy, and flexibility (emphasis on remote work options). 

In Conclusion…  

Workplace values are the most important guiding principles for how, when, and why employees work. Over time, these values have become increasingly progressive in the workforce, transforming from work-centric ideologies to person-first mindsets. Where Baby Boomers were content with devoting their lives to the work they found, Millennials and Gen Z seek purposeful devotions that serve both themselves and the communities they care about. For employers, understanding how the changing times result in generations with different workplace and worker values will not only help to better understand your employees but will also help to ensure the workplace environment you cultivate attracts, retains, and empowers all of your people.  

References 

Francis, Tracy, and Fernanda Hoefel.  ‘True Gen’: Generation Z and its implications for companies.  ,  https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/true-gen-generation-z-and-its-implications-for-companies.  

Herrity, Jennifer.  4 Common Characteristics of Generation X Professionals.  , 2022, https://www.indeed.com/career-advice/career-development/generation-x-professional-characteristics. 

Indeed Editorial Team.  10 Common Characteristics of the Millennial Generation.  , 2022, https://www.indeed.com/career-advice/interviewing/10-millennial-generation-characteristics. 

Kane, Sally.  Baby Boomers in the Workplace: How Their Generational Traits and Characteristics Affect the Workplace.  , 2019, https://www.liveabout.com/baby-boomers-2164681. 

Kasser, Tim, Richard Koestner, and Natasha Lekes. “Early Family Experiences and Adult Values: A 26-Year, Prospective Longitudinal Study  .”  Personality and Social Psychology Bulletin , vol. 28, no. 6, 2022,  https://journals.sagepub.com/doi/abs/10.1177/0146167202289011?journalCode=pspc, doi:https://doi.org/10.1177/0146167202289011. 

Martic, Kristina.  Millennials in the Workplace: 11 Ways to Attract and Keep Them.  , 2022,  https://haiilo.com/blog/millennials-in-the-workplace-11-ways-to-attract-and-keep-them/.  

Smith, Robert.  Generation X: History and Characteristics.  , 2021, https://www.familysearch.org/en/blog/generation-x-characteristics-history. 

U.S. BUREAU OF LABOR STATISTICS.  Employment status of the civilian noninstitutional population by age, sex, and race.  , 2022, https://www.bls.gov/cps/cpsaat03.htm. 

Zurich Editors.  How will Gen Z change the workplace?  , 2022, https://www.zurich.com/en/media/magazine/2022/how-will-gen-z-change-the-future-of-work. 

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It’s Time to Stop Talking About “Generations”

The discovery that you can make money marketing merchandise to teen-agers dates from the early nineteen-forties, which is also when the term “youth culture” first appeared in print. There was a reason that those things happened when they did: high school. Back in 1910, most young people worked; only fourteen per cent of fourteen- to seventeen-year-olds were still in school. In 1940, though, that proportion was seventy-three per cent. A social space had opened up between dependency and adulthood, and a new demographic was born: “youth.”

The rate of high-school attendance kept growing. By 1955, eighty-four per cent of high-school-age Americans were in school. (The figure for Western Europe was sixteen per cent.) Then, between 1956 and 1969, college enrollment in the United States more than doubled, and “youth” grew from a four-year demographic to an eight-year one. By 1969, it made sense that everyone was talking about the styles and values and tastes of young people: almost half the population was under twenty-five.

Today, a little less than a third of the population is under twenty-five, but youth remains a big consumer base for social-media platforms, streaming services, computer games, music, fashion, smartphones, apps, and all kinds of other goods, from motorized skateboards to eco-friendly water bottles. To keep this market churning, and to give the consulting industry something to sell to firms trying to understand (i.e., increase the productivity of) their younger workers, we have invented a concept that allows “youth culture” to be redefined periodically. This is the concept of the generation.

The term is borrowed from human reproductive biology. In a kinship structure, parents and their siblings constitute “the older generation”; offspring and their cousins are “the younger generation.” The time it takes, in our species, for the younger generation to become the older generation is traditionally said to be around thirty years. (For the fruit fly, it’s ten days.) That is how the term is used in the Hebrew Bible, and Herodotus said that a century could be thought of as the equivalent of three generations.

Around 1800, the term got transplanted from the family to society. The new idea was that people born within a given period, usually thirty years, belong to a single generation. There is no sound basis in biology or anything else for this claim, but it gave European scientists and intellectuals a way to make sense of something they were obsessed with, social and cultural change. What causes change? Can we predict it? Can we prevent it? Maybe the reason societies change is that people change, every thirty years.

Before 1945, most people who theorized about generations were talking about literary and artistic styles and intellectual trends—a shift from Romanticism to realism, for example, or from liberalism to conservatism. The sociologist Karl Mannheim, in an influential essay published in 1928, used the term “generation units” to refer to writers, artists, and political figures who self-consciously adopt new ways of doing things. Mannheim was not interested in trends within the broader population. He assumed that the culture of what he called “peasant communities” does not change.

Nineteenth-century generational theory took two forms. For some thinkers, generational change was the cause of social and historical change. New generations bring to the world new ways of thinking and doing, and weed out beliefs and practices that have grown obsolete. This keeps society rejuvenated. Generations are the pulse of history. Other writers thought that generations were different from one another because their members carried the imprint of the historical events they lived through. The reason we have generations is that we have change, not the other way around.

There are traces of both the pulse hypothesis and the imprint hypothesis in the way we talk about generations today. We tend to assume that there is a rhythm to social and cultural history that maps onto generational cohorts, such that each cohort is shaped by, or bears the imprint of, major historical events—Vietnam, 9/11, COVID . But we also think that young people develop their own culture, their own tastes and values, and that this new culture displaces the culture of the generation that preceded theirs.

Today, the time span of a generational cohort is usually taken to be around fifteen years (even though the median age of first-time mothers in the U.S. is now twenty-six and of first-time fathers thirty-one). People born within that period are supposed to carry a basket of characteristics that differentiate them from people born earlier or later.

This supposition requires leaps of faith. For one thing, there is no empirical basis for claiming that differences within a generation are smaller than differences between generations. (Do you have less in common with your parents than with people you have never met who happen to have been born a few years before or after you?) The theory also seems to require that a person born in 1965, the first year of Generation X, must have different values, tastes, and life experiences from a person born in 1964, the last year of the baby-boom generation (1946-64). And that someone born in the last birth year of Gen X, 1980, has more in common with someone born in 1965 or 1970 than with someone born in 1981 or 1990.

Everyone realizes that precision dating of this kind is silly, but although we know that chronological boundaries can blur a bit, we still imagine generational differences to be bright-line distinctions. People talk as though there were a unique DNA for Gen X—what in the nineteenth century was called a generational “entelechy”—even though the difference between a baby boomer and a Gen X-er is about as meaningful as the difference between a Leo and a Virgo.

You could say the same things about decades, of course. A year is, like a biological generation, a measurable thing, the time it takes the Earth to orbit the sun. But there is nothing in nature that corresponds to a decade—or a century, or a millennium. Those are terms of convenience, determined by the fact that we have ten fingers.

Yet we happily generalize about “the fifties” and “the sixties” as having dramatically distinct, well, entelechies. Decade-thinking is deeply embedded. For most of us, “She’s a seventies person” carries a lot more specific information than “She’s Gen X.” By this light, generations are just a novel way of slicing up the space-time continuum, no more arbitrary, and possibly a little less, than decades and centuries. The question, therefore, is not “Are generations real?” The question is “Are they a helpful way to understand anything?”

Bobby Duffy, the author of “The Generation Myth” (Basic), says yes, but they’re not as helpful as people think. Duffy is a social scientist at King’s College London. His argument is that generations are just one of three factors that explain changes in attitudes, beliefs, and behaviors. The others are historical events and “life-cycle effects,” that is, how people change as they age. His book illustrates, with a somewhat overwhelming array of graphs and statistics, how events and aging interact with birth cohort to explain differences in racial attitudes, happiness, suicide rates, political affiliations—you name it, for he thinks that his three factors explain everything.

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Duffy’s over-all finding is that people in different age groups are much more alike than all the talk about generations suggests, and one reason for all that talk, he thinks, is the consulting industry. He says that, in 2015, American firms spent some seventy million dollars on generational consulting (which doesn’t seem that much, actually). “What generational differences exist in the workplace?” he asks. His answer: “Virtually none.”

Duffy is good at using data to take apart many familiar generational characterizations. There is no evidence, he says, of a “loneliness epidemic” among young people, or of a rise in the rate of suicide. The falling off in sexual activity in the United States and the U.K. is population-wide, not just among the young.

He says that attitudes about gender in the United States correlate more closely with political party than with age, and that, in Europe, anyway, there are no big age divides in the recognition of climate change. There is “just about no evidence,” he says, that Generation Z (1997-2012, encompassing today’s college students) is more ethically motivated than other generations. When it comes to consumer boycotts and the like, “ ‘cancel culture’ seems to be more of a middle-age thing.” He worries that generational stereotypes—such as the characterization of Gen Z-ers as woke snowflakes—are promoted in order to fuel the culture wars.

The woke-snowflake stereotype is the target of “Gen Z, Explained” (Chicago), a heartfelt defense of the values and beliefs of contemporary college students. The book has four authors, Roberta Katz, Sarah Ogilvie, Jane Shaw, and Linda Woodhead—an anthropologist, a linguist, a historian, and a sociologist—and presents itself as a social-scientific study, including a “methodological appendix.” But it resembles what might be called journalistic ethnography: the portrayal of social types by means of interviews and anecdotes.

The authors adopt a key tenet of the pulse hypothesis. They see Gen Z-ers as agents of change, a generation that has created a youth culture that can transform society. (The fact that when they finished researching their book, in 2019, roughly half of Gen Z was under sixteen does not trouble them, just as the fact that at the time of Woodstock, in 1969, more than half the baby-boom generation was under thirteen doesn’t prevent people from making generalizations about the baby boomers.)

Their book is based on hour-long interviews with a hundred and twenty students at three colleges, two in California (Stanford and Foothill College, a well-regarded community college) and one in the U.K. (Lancaster, a selective research university). The authors inform us that the interviewees were chosen “by word of mouth and personal networking,” which sounds a lot like self-selection. It is, in any event (as they unapologetically acknowledge), hardly a randomized sample.

The authors tell us that the interviews were conducted entirely by student research assistants, which means that, unless the research assistants simply read questions off a list, there was no control over the depth or the direction of the interviews. There were also some focus groups, in which students talked about their lives with, mostly, their friends, an exercise performed in an echo chamber. Journalists, or popular ethnographers, would at least have met and observed their subjects. It’s mystifying why the authors felt a need to distance themselves in this way, given how selective their sample was to begin with. We are left with quotations detached from context. Self-reporting is taken at face value.

The authors supplemented the student interviews with a lexical glossary designed to pick out words and memes heavily used by young people, and with two surveys, designed by one of the authors (Woodhead) and conducted by YouGov, an Internet polling company, of eighteen- to twenty-five-year-olds in the United States and the U.K.

Where there is an awkward discrepancy between the survey results and what the college students say in the interviews, the authors attempt to explain it away. The YouGov surveys found that ninety-one per cent of all persons aged eighteen to twenty-five, American and British, identify as male or female, and only four per cent as gender fluid or nonbinary. (Five per cent declined to answer.) This does not match the impression created by the interviews, which suggest that there should be many more fluid and nonbinary young people out there, so the authors say that we don’t really know what the survey respondents meant by “male” and “female.” Well, then, maybe they should have been asked.

The authors attribute none of the characteristics they identify as Gen Z to the imprint of historical events—with a single exception: the rise of the World Wide Web. Gen Z is the first “born digital” generation. This fact has often been used to stereotype young people as screen-time addicts, captives of their smartphones, obsessed with how they appear on social media, and so on. The Internet is their “culture.” They are trapped in the Web. The authors of “Gen Z, Explained” emphatically reject this line of critique. They assure us that Gen Z-ers “understand both the potential and the downside of technology” and possess “critical awareness about the technology that shapes their lives.”

For the college students who were interviewed (although not, evidently, for the people who were surveyed), a big part of Gen Z culture revolves around identity. As the authors put it, “self-labeling has become an imperative that is impossible to escape.” This might seem to suggest a certain degree of self-absorption, but the authors assure us that these young people “are self-identified and self-reliant but markedly not self-centered, egotistical, or selfish.”

“Lily” is offered to illustrate the ethical richness of this new concern. It seems that Lily has a friend who is always late to meet with her: “She explained that while she of course wanted to honor and respect his unique identity, choices, and lifestyle—including his habitual tardiness—she was also frustrated by how that conflicted with her sense that he was then not respecting her identity and preference for timeliness.” The authors do not find this amusing.

The book’s big claim is that Gen Z-ers “may well be the heralds of new attitudes and expectations about how individuals and institutions can change for the better.” They have come up with new ways of working (collaborative), new forms of identity (fluid and intersectional), new concepts of community (diverse, inclusive, non-hierarchical).

Methodology aside, there is much that is refreshing here. There is no reason to assume that younger people are more likely to be passive victims of technology than older people (that assumption is classic old person’s bias), and it makes sense that, having grown up doing everything on a computer, Gen Z-ers have a fuller understanding of the digital universe than analog dinosaurs do. The dinosaurs can say, “You don’t know what you’re missing,” but Gen Z-ers can say, “You don’t understand what you’re getting.”

The claim that addiction to their devices is the cause of a rise in mental disorders among teen-agers is a lot like the old complaint that listening to rock and roll turns kids into animals. The authors cite a recent study (not their own) that concludes that the association between poor mental health and eating potatoes is greater than the association with technology use. We’re all in our own fishbowls. We should hesitate before we pass judgment on what life is like in the fishbowls of others.

The major problem with “Gen Z, Explained” is not so much the authors’ fawning tone, or their admiration for the students’ concerns—“environmental degradation, equality, violence, and injustice”—even though they are the same concerns that almost everyone in their social class has, regardless of age. The problem is the “heralds of a new dawn” stuff.

“A crisis looms for all unless we can find ways to change,” they warn. “Gen Zers have ideas of the type of world they would like to bring into being. By listening carefully to what they are saying, we can appreciate the lessons they have to teach us: be real, know who you are, be responsible for your own well-being, support your friends, open up institutions to the talents of the many, not the few, embrace diversity, make the world kinder, live by your values.”

I believe we have been here before, Captain. Fifty-one years ago, The New Yorker ran a thirty-nine-thousand-word piece that began:

There is a revolution under way . . . It is now spreading with amazing rapidity, and already our laws, institutions, and social structure are changing in consequence. Its ultimate creation could be a higher reason, a more human community, and a new and liberated individual. This is the revolution of the new generation.

The author was a forty-two-year-old Yale Law School professor named Charles Reich, and the piece was an excerpt from his book “The Greening of America,” which, when it came out, later that year, went to No. 1 on the Times best-seller list.

Reich had been in San Francisco in 1967, during the so-called Summer of Love, and was amazed and excited by the flower-power wing of the counterculture—the bell-bottom pants (about which he waxes ecstatic in the book), the marijuana and the psychedelic drugs, the music, the peace-and-love life style, everything.

He became convinced that the only way to cure the ills of American life was to follow the young people. “The new generation has shown the way to the one method of change that will work in today’s post-industrial society: revolution by consciousness,” he wrote. “This means a new way of living, almost a new man. This is what the new generation has been searching for, and what it has started to achieve.”

So how did that work out? The trouble, of course, was that Reich was basing his observations and predictions on, to use Mannheim’s term, a generation unit—a tiny number of people who were hyperconscious of their choices and values and saw themselves as being in revolt against the bad thinking and failed practices of previous generations. The folks who showed up for the Summer of Love were not a representative sample of sixties youth.

Most young people in the sixties did not practice free love, take drugs, or protest the war in Vietnam. In a poll taken in 1967, when people were asked whether couples should wait to have sex until they were married, sixty-three per cent of those in their twenties said yes, virtually the same as in the general population. In 1969, when people aged twenty-one to twenty-nine were asked whether they had ever used marijuana, eighty-eight per cent said no. When the same group was asked whether the United States should withdraw immediately from Vietnam, three-quarters said no, about the same as in the general population.

Most young people in the sixties were not even notably liberal. When people who attended college from 1966 to 1968 were asked which candidate they preferred in the 1968 Presidential election, fifty-three per cent said Richard Nixon or George Wallace. Among those who attended college from 1962 to 1965, fifty-seven per cent preferred Nixon or Wallace, which matched the results in the general election.

The authors of “Gen Z, Explained” are making the same erroneous extrapolation. They are generalizing on the basis of a very small group of privileged people, born within five or six years of one another, who inhabit insular communities of the like-minded. It’s fine to try to find out what these people think. Just don’t call them a generation.

Most of the millions of Gen Z-ers may be quite different from the scrupulously ethical, community-minded young people in the book. Duffy cites a survey, conducted in 2019 by a market-research firm, in which people were asked to name the characteristics of baby boomers, Gen X-ers, millennials (1981-96), and Gen Z-ers. The top five characteristics assigned to Gen Z were: tech-savvy, materialistic, selfish, lazy, and arrogant. The lowest-ranked characteristic was ethical. When Gen Z-ers were asked to describe their own generation, they came up with an almost identical list. Most people born after 1996 apparently don’t think quite as well of themselves as the college students in “Gen Z, Explained” do.

In any case, “explaining” people by asking them what they think and then repeating their answers is not sociology. Contemporary college students did not invent new ways of thinking about identity and community. Those were already rooted in the institutional culture of higher education. From Day One, college students are instructed about the importance of diversity, inclusion, honesty, collaboration—all the virtuous things that the authors of “Gen Z, Explained” attribute to the new generation. Students can say (and some do say) to their teachers and their institutions, “You’re not living up to those values.” But the values are shared values.

And they were in place long before Gen Z entered college. Take “intersectionality,” which the students in “Gen Z, Explained” use as a way of refining traditional categories of identity. That term has been around for more than thirty years. It was coined (as the authors note) in 1989, by the law professor Kimberlé Crenshaw. And Crenshaw was born in 1959. She’s a boomer.

“Diversity,” as an institutional priority, dates back even farther. It played a prominent role in the affirmative-action case of Regents of the University of California v. Bakke, in 1978, which opened the constitutional door to race-conscious admissions. That was three “generations” ago. Since then, almost every selective college has worked to achieve a diverse student body and boasts about it when it succeeds. College students think of themselves and their peers in terms of identity because of how the institution thinks of them.

People who went to college in an earlier era may find this emphasis a distraction from students’ education. Why should they be constantly forced to think about their own demographic profiles and their differences from other students? But look at American politics—look at world politics—over the past five years. Aren’t identity and difference kind of important things to understand?

And who creates “youth culture,” anyway? Older people. Youth has agency in the sense that it can choose to listen to the music or wear the clothing or march in the demonstrations or not. And there are certainly ground-up products (bell-bottoms, actually). Generally, though, youth has the same degree of agency that I have when buying a car. I can choose the model I want, but I do not make the cars.

Failure to recognize the way the fabric is woven leads to skewed social history. The so-called Silent Generation is a particularly outrageous example. That term has come to describe Americans who went to high school and college in the nineteen-fifties, partly because it sets up a convenient contrast to the baby-boom generation that followed. Those boomers, we think—they were not silent! In fact, they mostly were.

The term “Silent Generation” was coined in 1951, in an article in Time —and so was not intended to characterize the decade. “Today’s generation is ready to conform,” the article concluded. Time defined the Silent Generation as people aged eighteen to twenty-eight—that is, those who entered the workforce mostly in the nineteen-forties. Though the birth dates of Time’s Silent Generation were 1923 to 1933, the term somehow migrated to later dates, and it is now used for the generation born between 1928 and 1945.

So who were these silent conformists? Gloria Steinem, Muhammad Ali, Tom Hayden, Abbie Hoffman, Jerry Rubin, Nina Simone, Bob Dylan, Noam Chomsky, Philip Roth, Susan Sontag, Martin Luther King, Jr., Billie Jean King, Jesse Jackson, Joan Baez, Berry Gordy, Amiri Baraka, Ken Kesey, Huey Newton, Jerry Garcia, Janis Joplin, Jimi Hendrix, Andy Warhol . . . Sorry, am I boring you?

It was people like these, along with even older folks, like Timothy Leary, Allen Ginsberg, and Pauli Murray, who were active in the culture and the politics of the nineteen-sixties. Apart from a few musicians, it is hard to name a single major figure in that decade who was a baby boomer. But the boomers, most of whom were too young then even to know what was going on, get the credit (or, just as unfairly, the blame).

Mannheim thought that the great danger in generational analysis was the elision of class as a factor in determining beliefs, attitudes, and experiences. Today, we would add race, gender, immigration status, and any number of other “preconditions.” A woman born to an immigrant family in San Antonio in 1947 had very different life chances from a white man born in San Francisco that year. Yet the baby-boom prototype is a white male college student wearing striped bell-bottoms and a peace button, just as the Gen Z prototype is a female high-school student with spending money and an Instagram account.

For some reason, Duffy, too, adopts the conventional names and dates of the postwar generations (all of which originated in popular culture). He offers no rationale for this, and it slightly obscures one of his best points, which is that the most formative period for many people happens not in their school years but once they leave school and enter the workforce. That is when they confront life-determining economic and social circumstances, and where factors like their race, their gender, and their parents’ wealth make an especially pronounced difference to their chances.

Studies have consistently indicated that people do not become more conservative as they age. As Duffy shows, however, some people find entry into adulthood delayed by economic circumstances. This tends to differentiate their responses to survey questions about things like expectations. Eventually, he says, everyone catches up. In other words, if you are basing your characterization of a generation on what people say when they are young, you are doing astrology. You are ascribing to birth dates what is really the result of changing conditions.

Take the boomers: when those who were born between 1946 and 1952 entered the workforce, the economy was surging. When those who were born between 1953 and 1964 entered it, the economy was a dumpster fire. It took longer for younger boomers to start a career or buy a house. People in that kind of situation are therefore likely to register in surveys as “materialistic.” But it’s not the Zeitgeist that’s making them that way. It’s just the business cycle. ♦

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Old Versus Young: The Cultural Generation Gap

Younger, more diverse generations promise to change all aspects of American society.

In this Issue:

• Winter 2018 View All Other Issues
• Generations: People are living and working longer—changing the world
• Six Generations Moving Forward Together
• Crunch: Definining Generations
• Foreword: How Are Generations Named?
• The Generation Gap
• Getting More From a Longer Life
• Five Questions: Teaching the Next Generation
• Lessons From the Greatest Generation
• As the World Ages
• Generation X and the American Dream
• Millennials Aren’t Kids Anymore
• View All Other Issues

If demography is destiny, the United States—much more than its peers—is on the cusp of great change. That change is due to a deep cultural generation gap at play, which will alter all aspects of American society within the coming decade.

Driving this generational gap is a “diversity explosion” in the United States, which began in 2011 when, for the first time in the history of the country, more minority babies than white babies were born in a year. Soon, most children in the U.S. will be racial minorities: Hispanics, blacks, Asians, and other nonwhite races. And, in about three decades, whites will constitute a minority of all Americans. This milestone signals the beginning of a transformation from the mostly white baby-boom culture that dominated the nation during the last half of the 20th century to the more globalized, multiracial country that the United States is becoming.   

As the younger, more diverse part of the population reaches adulthood, clear gaps will develop between its economic interests and politics and those of the whiter, older generations . This divide will result in contests over local expenditures—for example, over whether to spend money on schools or senior health facilities—and those contests may evolve into culture clashes. Yet if demography is truly destiny, America's workforce, politics, and place on the world stage will soon be changed forever.

Data Points

America's “new minorities”—particularly Hispanics and Asians —are becoming an increasingly strong thread in the social fabric of the United States. While this has been growing clearer for some time, recent information from the census and elsewhere shows how quickly these minorities are transforming the character of the nation’s youth. Consider the change in the U.S. population under age 18 in the first decade of the 2000s: From 2000 to 2010, the population of white children declined by 4.3 million while the child population in each of the newer minority groups—Hispanics, Asians, and people of two or more races—increased. Hispanics registered the largest absolute increase in children , 4.8 million. Were it not for Hispanics, the nation’s child population would have declined. And in 2010, slightly more than half of children under age 5 were white, while the oldest age group—those 85 and older—was 85 percent white. This diversification of the U.S. population from the bottom up holds more than just demographic significance. It reflects an emerging cultural divide between the young and the old as they adapt to change in different ways. Different age groups represent different generations, which were raised and became adults in specific eras and may be more or less receptive to the cultural changes brought about by new racial groups. 

When viewed broadly, there is a sharp racial distinction between the baby boomers and their elders, and the younger generations—the millennials and young members of Generation X and their children, who constitute the population under the age of 35. Baby boomers and seniors are more than 70 percent white, with blacks representing the largest racial minority. In con-trast, millennials and young Gen Xers (largely under the age of 35) and their children are more than 40 percent minority, with Hispanics constituting the largest share of their minority population. A 2011 Pew Research Center poll shows that only 23 percent of baby boomers and seniors regard the country’s growing population of immigrants as a change for the better and that 42 percent see it as a change for the worse. More than one-half of white baby boomers and seniors said the growing number of newcomers from other countries represents a threat to traditional U.S. values and customs. 

The resistance of baby boomers to demographic change may seem surprising. This much-celebrated generation came to embody the image of middle America during the second half of the last century. Conceived during the prosperous post−World War II period, they brought a rebellious, progressive sensibility to the country in the 1960s, 1970s, and beyond. With the help of the programs of the Great Society, they became the most well-schooled generation to date and the epitome of America’s largely white, suburban middle class, with which most of today’s adults now identify. 

Yet the baby boomers also came of age at a moment when the United States was becoming more insular than it had been before. Growing up in mostly white, segregated suburbs, white baby boomers had less exposure to immigrants and foreign wars than their parents did. Between 1946 and 1964, the years of the baby boom, the immigrant share of the U.S. population shrank to an all-time low (under 5 percent), and the immigrants who did arrive were largely white Europeans. Although baby boomers were interested in righting domestic wrongs, such as racial discrimination, and busting glass ceilings in the workplace, they did not have much interaction with people from other countries. The cultural generation gap continues to appear when baby boomers and seniors are compared with the younger segment of the U.S. population, whose members are more likely to be first- or second-generation Americans of non-European ancestry and to be bilingual.

Between 1946 and 1964, the years of the baby boom, the immigrant share of the U.S. population shrank to an all-time low (under 5 percent), and the immigrants who did arrive were largely white Europeans.

Underpinning the generational divide are shifts in what demographers call old-age dependency (the population age 65 and over as a percent of the labor force–age population) and child dependency (the population under age 18 as a percent of the labor force–age population), which now have a distinct racial dimension. Both historically and internationally, the number of children dependent on the labor force–age population has been larger than the number of dependent retirees. However, in quickly aging countries where birth rates are declining and life expectancy is rising, seniors are increasing the numbers of the “dependent” population. That is of concern in the United States, given that government programs aiding the elderly, including those for medical care, cost substantially more than those aiding children. The cultural generation gap between the young and the old can exacerbate the competition for resources because the rise in the number of senior dependents is occurring more rapidly among whites than among minorities, for whom dependent children is a larger issue.

A look at the total U.S. population helps illustrate this. The growth of the senior population is affected by increased life expectancy and, more importantly, the aging of the baby boomers. From 2010 to 2030, the senior population is projected to grow by 84 percent. In contrast, the labor force–age population (ages 18 to 64) will grow by only 8 percent and the population under age 18 will grow by just 3 percent. Therefore, although new minorities and immigrants are driving the increases in the younger and labor force-age populations, the growth of the senior population is driven by the mostly white baby boomers. The dependency ratios show the shifts expected by 2040. Youth dependency was almost twice the level of old-age dependency in 2010 (38 versus 21) and will increase only slightly during the following three decades, while old-age dependency will rise by well over one-half—making seniors a substantial portion of the non-working-age population.

Yet this shift is far more dramatic for whites than for minorities. The comparison of dependen-cy ratios for whites and Hispanics shows their likely relative priorities with regard to spending on children versus seniors. For whites, youth dependency is lower than the U.S. total and is not much larger than white old-age dependency in 2010 (32 versus 26). In fact, by 2020, the old-age dependency ratio for whites will exceed the child dependency ratio, and for the two decades that follow, white seniors will outnumber white children. That stands in marked contrast to Hispanics, whose 2010 youth dependency ratio was 56 and whose old-age dependency ratio was only 9. Moreover, Hispanic youth dependency will remain well above 40 through 2040, even as the old-age dependency ratio inches up to 22. In other words, for at least the next three decades, Hispanic children will sharply outnumber Hispanic seniors. Although black and Asian youth dependency is not as marked as it is for Hispanics, it remains higher than senior dependency through at least 2030. Therefore there is no question that the primary concern of working-age Hispanics—and to a lesser extent Asians and blacks—will be their children rather than the older dependent population. For working-age whites, elderly dependents will be a primary concern as well as their own future well-being as they enter their retirement years. This demographic framework provides a concrete basis for considering the cultural generation gap and competition for government resources allocated to children and the elderly.

In discussing the long-term political ramifications of the generation gap, political writer Ronald Brownstein has framed it as a divide between “the gray and the brown,” wherein older whites, including aging baby boomers, favor smaller government investment in social support programs except for those, such as Social Security, that directly affect them. For these older voters, big government is associated with higher taxes, which primarily benefit younger demographic groups whose needs they do not fully appreciate. In contrast, surveys show that more diverse youth, particularly millennials, tend to support greater government spending on education, health, and social welfare programs that strongly affect young families and children. 

It is important for retiring baby boomers to understand that the solvency of government-supported retirement and medical care programs is directly dependent on the future productivity and payroll tax contributions of a workforce in which minorities, especially Hispanics, will dominate future growth. There is a well-recognized challenge in providing these future workers with the skills needed to make these contributions, and meeting that challenge requires public investment in education and related services. The dilemma, however, is that the largest government programs that directly benefit the elderly, such as Social Security and Medicare , are mostly financed by the federal government and are considered politically sacred by many. In contrast, programs for youth, such as education, are largely funded at the state and local levels and are far more vulnerable to economic downturns and budget cuts given that states, unlike the federal government, are required to balance their budgets annually. Therefore efforts to muster support for child-oriented programs require grassroots support across an often frag-mented political terrain. In the future, more young minorities will enter their prime voting years and both national political parties will need to balance the needs and concerns of new and old voters, particularly in regions of the country where the cultural generation gap is emerging.

"The cultural generation gap between the young and the old can exacerbate the competition for resources because the rise in the number of senior dependents is occurring more rapidly among whites than among minorities, for whom dependent children is a larger issue."

Although this gap is forming throughout the nation, the growth of the young new minority population and the steadier gains of the aging white population are occurring at different speeds in different regions. The most racially diverse and youthful populations are in states and met-ropolitan areas in the Southwest, Southeast, and major urban immigration centers where new minorities have had an established presence. A shorthand measure for what is happening in a state or metropolitan area is the difference between the percentage of seniors who are white and the percentage of children who are white. In 2010, 80 percent of the U.S. senior population and 54 percent of children were white, so the national gap was 26 percent. But among states, Arizona led the way, with a gap of 41 percent (83 percent of seniors and 42 percent of children were white). Nevada, California, New Mexico, Texas, and Florida were not far behind, with gap measures greater than 30. Among major metropolitan areas, the largest gaps were in Riverside, California; Phoenix; Las Vegas; and Dallas.

In contrast, large—mostly white—swaths of the country, including the noncoastal Northeast, Midwest, and Appalachia, are observing slow growth or even declines in their youth popula-tions while remaining home to large numbers of white baby boomers and seniors. The demo-graphic profiles of these regions, along with those of metropolitan areas such as Pittsburgh, Cincinnati, and St. Louis, will eventually converge with those of more diverse parts of the country. But in the interim, they will be adapting, often fitfully, to the changes occurring elsewhere. 

Still, the places where the cultural generation gap has generated the most contention are those where the gains in new minorities are large and recent. Arizona is emblematic because of its large gap and recent Hispanic growth of 175 percent from 1990 to 2010. In 2010, the state passed one of the strictest anti-immigration laws ever enacted, though it was later amended and portions of the law were struck down by the U.S. Supreme Court. Provisions included requirements that residents carry papers verifying their citizenship; if they did not, they would be subject to arrest, detention, and potential deportation. 

A statewide poll taken at the time split along racial lines: Sixty-five percent of whites but only 21 percent of Hispanics were in favor of the new law. Similarly, the law was favored by 62 percent of those 55 and older (across all races) but only 45 percent of those under 35. Later, other states with recent Hispanic or new immigrant population gains, including Alabama, Georgia, South Carolina, and Utah, proposed similarly strict immigration laws. 

As young new minorities continue to disperse outward from traditional gateways, the cultural generation gap will appear in communities of all sizes, but it will be widest in states where the growth of young minorities is new and the racial demographic profile of the younger generation differs most from that of the older generation.

Thus, on a variety of levels, the continuing spread of new minorities from the bottom up of the nation’s age distribution creates important opportunities for the growth and productivity of the nation’s population and workforce. But that spread also presents challenges in light of the sharp cultural shift that is taking place. The divide will require adaptation on all sides, and policymakers and citizens alike will need to approach these changes with a long view. Rather than seeing the inevitable changes as damaging to the American way of life, it will behoove the nation to consider the future of the country and prepare now for a country that will be majority-minority. 

William H. Frey is a senior fellow at the Brookings Institution and research professor with the Population Studies Center and Institute for Social Research at the University of Michigan. He is author of Diversity Explosion: How New Racial Demographics Are Remaking America, from which this essay is adapted.

MORE FROM PEW

Making generational differences work: What empirical research reveals about leading millennials

Generational differences – reality versus rumor

The topic of generational differences is one that never seems to go away. There has always been a certain amount of narrative and debate about what the differences are and how they influence organizational dynamics. Yet it is important to bear in mind that generational categories are somewhat arbitrary social constructs and that shifting patterns of motivation and behavior are a natural function of a world that itself is changing rapidly. Furthermore, people’s notion of any given generation is based on stereotyping, subjective perception and untested assumptions.

Millennials, aka Generation Y, are the fastest-growing organizational population globally. Yet how much do business leaders understand about them beyond general perceptions, and what are these perceptions based upon? Are there any empirical data to validate or challenge these assumptions?

One thing is certain – the environment in which people grow up influences their expectations and behavioral norms and, consequently, how they categorize and interpret the behaviors of others. People tend to measure performance relative to their own expectations, and this alone is an important consideration when evaluating generational differences.

In an attempt to provide more factual, specific and measurable information on the topic, Management Research Group conducted a global study of generational differences involving over 23,000 leaders across Europe, North America and Asia- Pacific. The aim of the study was to move beyond the general mythology of generational differences and to identify the exact ways in which motivational characteristics vary.

Research methodology

A total of 23,298 leaders completed the Individual Directions InventoryTM (IDI) assessment. The IDI is an expert psychometric assessment that measures 17 underlying and intrinsic motivational characteristics using a sophisticated semi-ipsative normative questionnaire design. This article presents the findings within Europe (n=9,450) across four generations:

Baby Boomers – mid-1940s to mid-’60s (n=3,263)

Gen X – mid-’60s to late ’70s (n=4,623)

Gen Y – early ’80s to early ’90s (n=1,472)

Gen Z – early ’90s to date (n=92)

The median value of each generation in the 17 motivational scales was calculated and plotted on a line graph. For the purposes of comparability, the data in each of the three global regions were compared against the General European norm (n=5,678). The 17 drivers measured in the IDI are organized into thematic groups (Affiliating, Attracting, Perceiving, etc.) to make it easier to understand key facets of the motivational profile.

There are a few caveats when dealing with generational differences. The research identifies the ways in which the motivation of Millennials varies from that of other generations. But people evolve as they learn and mature, so leaders need to be careful not to create a fixed concept of Millennials upon which important organizational decisions might be based. How different will they be 10 years from now? There is also a degree of variance within the Millennial population (as with other generations), so there will always be a need for some calibration in the way individuals are managed.

Understanding motivational DNA

Before examining the findings of this research, it is important to understand what these data are measuring. In this case, they are measuring intrinsic motivation – that is, the areas in which people derive an internal feeling of satisfaction and personal fulfillment. These characteristics originate from a person’s early years, the first 10 to 12 years being highly influential in shaping their unique motivational profile. Motivation does, however, evolve slowly over time; what people find personally rewarding and fulfilling in their early 20s compared with their 50s is likely to be different in some ways. Life experience, personal growth and broader perspective, among other factors, inevitably contribute to changes in what people find emotionally satisfying as they age.

It is important to note that this research does not measure behavior, so the findings do not describe what different generations do. This is interesting insofar as the only thing that can be observed about others is their behavior, but how much of their behavior reflects them intrinsically, and how much of it reflects the way they adapt to different environments? By measuring intrinsic motivation, the research is getting much closer to the truth of the individual, and in so doing measuring characteristics that are more stable over time and likely to reflect the person more than their context.

Baby Boomers and Gen X (mid-1940s to late 1970s)

Depending on how these generations are defined, together they cover a period from the mid-1940s to the late 1970s, a significant period of time in itself but all the more noteworthy when the degree of social, political and technological change is considered.

The pattern of distribution in Figure 1 clearly indicates that there are relatively minor differences between the median values in intrinsic motivation between Baby Boomers and Gen X. This is perhaps a little surprising considering both the span of time that these generations cover and the degree of change in world affairs. It suggests that it potentially takes a lot to change motivational DNA, or at least that the factors that really influence motivational drivers were not necessarily experienced during this period.

Figure 1: Individual Directions Inventory for Baby Boomers and Gen X

Millennials or Generation Y (early 1980s to early 1990s)

Much has been written about the Millennial generation, not all of it complementary. Time magazine in May 2013 described Millennials as “lazy, entitled narcissists who live with their parents.” While the content of the article was in fact reasonably balanced, in general there seems to be more focus on the negatives than on the positives. It is worth remembering that newer generations are simply different from those that preceded them; whether that makes them better or worse is a matter of opinion.

Millennials have lived through an era of phenomenal technological growth, including the emergence of the internet and social media. Like Generation Z, they have witnessed the relentlessness of global terrorism and unprecedented economic turmoil. The world is an uncertain, informationally overloaded, fast- moving place. Some countries in particular (e.g. Spain, Greece and Italy) are still experiencing the economic repercussions of the global credit crisis; how might this influence the core drivers of those who have grown up in such an environment?

Figure 2 shows key differences between Millennials and their predecessors, bearing in mind that the graph measures motivational DNA, not behavioral characteristics. Millennials have higher expectations of achievement (Excelling), bringing a greater sense of urgency to accelerated growth and career progression. Interestingly, there is strong evidence to suggest less originality in this generation than the stereotype might suggest. For example, Millennials are more motivated by a world that is safe and predictable (Stability) and less by environments that require them to innovate and to think in more lateral terms (Creating). This flies in the face of many preconceptions about this newer generation, particularly considering their immersion in new technology. But are they truly the originators, or merely the consumers of innovation?

Figure 2: Individual Directions Inventory for Baby Boomers, Gen X and Millennnials

The elevated median score on the Structuring scale is the most dramatic feature. This scale measures the extent to which the individual enjoys working in a manner that is meticulous, precise and systematic. It also indicates that more clarity about process and “how to” is critical for this generation, and that there might be the first signs of more compulsive tendencies emanating from this underlying motivational characteristic. The graph also indicates less enjoyment from assuming command (Controlling) or from working in a more autonomous, self-reliant manner (Independence). These point strongly toward a preference for more democratic, inclusive decision-making processes and a facilitative approach to leadership.

Motivational measures in themselves describe what people find personally satisfying and rewarding, so they can appear to be a very positive type of construct. But motivational drivers can easily conflict with each other, and often do. For example, some people are motivated by success and security at the same time, so the desire to achieve comes with an elevated fear of failure. This newer generation wants significant achievement (Excelling) with less inherent risk (Stability), and wants to do so in a manner that requires less self-sufficiency (Independence). Previous generations, particularly those whose work ethic revolves around “serving one’s time” and earning the right to be successful, might not appreciate these characteristics.

Practical steps for working with Millennials

Comparatively speaking, Millennials have a more significant informational need than their predecessors. They clearly like to be kept in the loop, finding an ongoing flow of information reassuring as well as informative . They also like to understand the “how to,” reflecting their need for others to be granular and specific in their instructions. They are likely to feel uncomfortable in “last-minute” scenarios, preferring some degree of advance notice to an extent that might be underestimated by others. If previous generations are unaware of these variances, it makes it more difficult for them to make good behavioral choices and to calibrate the ways in which they keep newer generations informed (and vice versa).

The same approach can be applied to other themes such as expectations and sensitivities in relationships. For example, Millennials (and Gen Z even more so) are more likely to be sensitive to situations in which they feel unsupported (Receiving). Newer generations also appear to manifest a greater degree of caution (higher on Stability and Structuring, lower on Creating), which suggests that they will be more comfortable driving innovation when they can do so collectively and with input and validation from others.

This research, especially the empirical measurement that it is based upon, provides practical insights and objective evidence about how to lead, manage and motivate employees across generations. Leaders would do well to act on the following guidelines:

• Be aware that a faster pace of progression and learning is important. Millennials have very high expectations of achievement, both in extent and pace. Establishing a clear career path, developmental stages and criteria for progression helps in this process. Be tangible and specific, not conceptual.
• Foster a more inclusive and democratic environment. Newer generations work best when they collaborate and exchange information and ideas continually. They are likely to feel less secure when they have to work autonomously, and may hold off making a decision until they have satisfied their higher level of informational needs.
• Avoid a “command and control” approach to leadership – it doesn’t work. A more facilitative style is more likely to coax the best out of Millennials; they are a highly motivated group so there is a great deal of positive energy to tap into. Delegate ownership, not just task. Keep a supportive eye on progress and provide ongoing feedback. Develop your “leader as mentor/coach” skills.
• Set clear expectations from the outset. Providing context, explaining method and defining objectives will make a positive difference. As before, be tangible and specific, not conceptual. Leave the door open to allow Millennials to double-check things as they execute.
• Provide ongoing support. Although previous generations might interpret supervision as micromanagement, Millennials are more likely to interpret it as support . They also expect more immediate access to information and support. They might feel more comfortable finding out for themselves if they can use a technology platform to support their learning or to provide immediate access to critical information.
• Develop an understanding of motivational characteristics. By being aware of their own expectations and biases and using a methodical approach to measuring motivation, leaders can try to bring the best out in others irrespective of their generational origin.

Looking ahead to Generation Z (early 1990s to date)

The changing patterns of motivation described above are intriguing in themselves, but are they one-off variances, or do they represent a specific trajectory that continues into the post-Millennial generation? Generation Z represents a much smaller population, both in this research study and in organizations in general, but it is worth including, if only to see where the motivational direction is heading.

The purple line in Figure 3 represents Generation Z; these findings are quite extraordinary and provide an early indication that the variance observed between Millennials and previous generations is not simply random. Something more fundamental is changing in motivational dynamics. What the causal factors are remains an open question; these results, particularly their consistency, are unlikely to be arbitrary. Therefore, a reasonable assumption is that something is happening environmentally that is propagating an unprecedented degree of change in motivational drivers. Hence the need for leaders to keep the focus on understanding generational differences and to adopt a more nuanced and thoughtful leadership style in the quest to enhance the performance of both individuals and the organization.

Figure 3: Individual Directions Inventory for Baby Boomers, Gen X, Millennnials and Gen Z

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The Generation Gap Revisited: Generational Differences in Mental Health, Maladaptive Coping Behaviors, and Pandemic-Related Concerns During the Initial COVID-19 Pandemic

Kaitlin grelle.

Department of Psychology, Texas State University, 601 University Drive, San Marcos, TX 78666 USA

Neha Shrestha

Megan ximenes, jessica perrotte, millie cordaro, rebecca g. deason, krista howard.

The purpose of this study was to assess differences in mental health symptoms, pandemic-related concerns, and maladaptive coping behaviors among adults in the United States across generations during the initial period of the COVID-19 pandemic. A social media campaign was used to recruit 2696 U.S. individuals to participate in an online survey in April 2020, assessing various validated psychosocial factors, including major depressive disorder, generalized anxiety disorder (GAD), perceived stress, loneliness, quality of life, and fatigue, along with pandemic-specific concerns and changes in alcohol use and substance use. Participants were grouped based on generation status (Gen Z, Millennial, Gen X, and Baby Boomer) and statistical comparisons were conducted based on demographics, psychosocial factors, pandemic-related concerns, and substance use. During the initial period of the COVID-19 pandemic, the younger cohorts (Gen Z and Millennials) rated significantly worse on mental health indices, including major depression, GAD, perceived stress, loneliness, quality of life, and fatigue. Further, the participants in the Gen Z and Millennial generational groups exhibited greater increase in maladaptive coping with substance use, specifically alcohol use and increased use of sleep aids. Our results indicate that during the initial period of the COVID-19 pandemic, members of the Gen Z and Millennial generational cohorts were considered a psychologically vulnerable population due to their mental health and maladaptive coping behaviors. Improving access to mental health resources during early stages of a pandemic is an emerging public health concern.

Introduction

In March 2020, the World Health Organization declared a global pandemic due to an outbreak of the COVID-19 virus (CDC, 2020). To reduce community transmission, the United States (U.S.) enacted broad community mitigation strategies, including nationwide stay-at-home orders for all non-essential workers (Howard et al., 2021 ; Salari et al., 2020 ; Xiong et al., 2020 ). These measures confined millions of individuals to their homes, while creating high-risk work environments for essential workers (U.S. Department of Labor, 2020 ). Social and psychological consequences associated with these mitigation efforts and the pandemic event itself are critical public health concerns (Bu et al., 2020 ; Hossain et al., 2020 ). The psychological distress created by these complex, multi-faceted disruptions quickly eroded mental health and well-being (Park et al., 2021 ), but these disruptions may not impact all age groups equally as a result of several factors (e.g., risk for disease, existing support systems, financial security, history-graded cohort influences, etc.).

Pandemics and epidemics have been documented as traumatic stressor events that evoke fear, confusion, and uncertainty regarding susceptibility, transmission, and treatment, while contributing to the onset of psychopathology and mental health disorders (Brooks et al., 2020 ; Wang et al., 2021a , 2021b ). Tuberculosis, HIV, and Polio endemics have been linked to acute psychological distress, including symptoms of depression and anxiety (Anjum et al., 2020 ; Bruno & Frick, 1991 ). During the COVID-19 pandemic, disruptions to day-to-day living negatively impacted individuals’ routines, social support networks, and coping resources (WHO, 2020). In addition, some individuals experienced social isolation and loneliness, which are both empirically linked to psychological distress (Ahmed et al., 2020 ; Ames-Guerrero et al., 2021 ; Anjum et al., 2020 ; Cosic et al., 2020; Moghanibashi-Mansourieh, 2020 ). Furthermore, economic disruptions magnified psychological distress and anxiety (Turchioe et al., 2021 ). In the U.S., more than 40 million people filed for unemployment as businesses closed while others grappled with layoffs or furloughs (Turchioe et al., 2021 ; U.S. Department of Labor, 2020 ). Collectively, these risk factors played a key role as traumatic stressors in developing or exacerbating maladaptive coping behaviors, psychopathology and/or mental health disorders among the U.S. population (Hossain et al., 2020 ).

Throughout the pandemic, symptoms of psychological dysfunction including increased anxiety, depression, and stress have been reported globally (Hossain et al., 2020 ; Huang & Zhao, 2020 ; Salari et al.,; 2020 ; Wang et al., 2021a , 2021b ; Xiong et al., 2020 ). At the onset of the pandemic in the U.S., Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD) both demonstrated significant increases in prevalence rates among the general population (Cordaro et al., 2021 ; Uwadiale et al., 2021 ). Given that older adults are at higher risk for severe COVID-19 infection (e.g., hospitalization or intensive care admission; CDC, 2021), there has been a growing concern regarding this population’s psychological vulnerability to mental health symptoms and disorders (Anjum et al., 2020 , El-Gabalaway et al., 2021; Turchioe et al., 2021 ). Research studies have previously implicated chronic health issues as significant stressors affecting mental health in older adults (Luo et al., 2021), and it is important to understand the implications for mental health across different age groups and cohorts during the early stages of the pandemic.

These cascading societal catastrophes related to the pandemic, in addition to the pandemic as a large-scale traumatic event itself, set the stage for collective trauma (Hirschberger, 2018 ). Yet, research shows that not all generational groups will experience mental health impacts from traumatic stress proportionately (Buffel et al., 2021 ). The lifespan developmental perspective has been applied to research on stress and coping processes (Baltes, 1987 ; Baltes et al., 2006 ; Spiro, 2007 ). This perspective describes how an individuals’ ability to effectively cope with stress is a lifelong process with developmental progression occurring for older adults belonging to the baby boomer cohort, compared to younger age groups belonging to younger cohorts. For example, Baby Boomers grew up in a post-World War II era and managed through wars, political upheaval, natural disasters, and other infectious epidemics (Lind et al., 2021 ). Therefore, older adults have a lifetime of experience with crises and resilience (Wettstein et al., 2022 ). Several recent studies have suggested that older adults have been more successful at navigating COVID-19 pandemic mental health concerns and maladaptive coping behaviors than younger age groups (Brotto et al, 2021 ; Bruine de Bruin, 2021 ). Older adults’ resilience and ability to cope with stressful situations is evidenced in several studies finding increased positive and decreased negative affect in older adults compared with younger adults (Fields et al., 2022 ; Klaiber et al., 2021 ). Early in the COVID-19 pandemic, older adults showed less reactivity overall to stressors than younger adults (Klaiber et al., 2021 ), similar to pre-pandemic findings suggesting older adults are more likely to use coping strategies to manage stressful situations (Charles et al. 2010). Although this work was based on age-related effects, it has been supported by generational cohort comparisons indicating that members of the Boomer generation had better mental health outcomes than Millennial and Gen X groups (Turchioe et al., 2021 ). It follows then, that although older adults have a higher risk for severe illness, yet, based on developmental processes, younger generational groups will be at higher risk for psychopathology exacerbated by the pandemic (Kiss et al., 2022 ). Recent research demonstrated that those in middle adulthood, characterized as a time of career and caregiving responsibilities, experienced increased symptoms of depression and sleep issues during the pandemic (Brown & Arigo, 2022 ). Recently, the U.S. Surgeon General issued an advisory on the pandemic-related mental health crisis unfolding for youth including emerging adults already challenged by foundational developmental tasks (Arnett, 2000 ; Office of the Surgeon General, 2021). In sum, research is showing disproportionate impacts to mental health, psychopathology, and coping for differing age groups and cohorts, yet few studies have made generational comparisons across groups.

The broad aim of this study is to describe self-reported changes in mental health symptoms and maladaptive substance use behaviors across different generations in the U.S. [i.e., Generation Z (Gen Z): born 1997–2012; Millennials: born 1981–1996; Generation X (Gen X): born 1965–1980; and Baby Boomers: born 1946–1964; PEW Research Center, 2019 ] during the initial period of the COVID-19 pandemic. Based on the limited literature on generational differences during the COVID-19 pandemic, along with the historical knowledge of Baby Boomers’ experiences, it is hypothesized that the older generational groups will have less psychosocial distress and maladaptive coping compared to the younger generations. Identifying these differences will improve understanding of how individuals from different generational groups responded during the initial part of the pandemic, which can inform public health initiatives in future events.

Participants and Procedure

Participants were recruited through a nationwide Facebook Sponsored Ads campaign between April 14 and April 22, 2020. The advertising posts were placed on random newsfeeds of participants ages 18 and older living in the U.S. During this recruitment period, 4406 individuals clicked on the recruitment post linked to the survey and 2739 of those individuals provided consent and completed the survey in Qualtrics. For the present study, 2696 participants provided their age and were included in the analyses. Participation was voluntary. The mean age of participants in the sample was 47.8 years (SD = 12.9) and 87.8% of the sample were female, and 89.9% were non-Hispanic white. The data were weighted to the total U.S. population based on the 2018 Census Bureau population estimates by age, sex, and race/ethnicity (U.S. Census Bureau, 2018 ). All participants provided written informed consent to participate in this study. The Institutional Review Board at Texas State University approved the protocol for this study (#7221).

All participants were classified into the generational groups based on their current ages at the time of data collection (PEW Research Center, 2019 ). Of the 2696 participants included in the present study, those in the Gen Z group were ages 18–23 ( n  = 86; 3.2%; 8.8% with population weights applied), the Millennial group were ages 24–39 years ( n  = 693; 25.7%; 25.9% with population weights applied), the Gen X group were ages 40–55 years (1086; 40.3%; 31.4% with population weights applied), and the Baby Boomer group were 56–74 years ( n  = 831; 30.8%; 33.9% with population weights applied).

Demographics

Participants reported age, gender identity, race/ethnicity, marital status, children, medical insurance, employment, and education level.

Psychosocial Measures

The perceived stress scale (pss).

The PSS (Cohen et al., 1983 ) is a 10-item measure using a 5-point Likert scale assessing general life stressors experienced in the past four weeks with responses ranging from Never to Very Often . An example item is, “How often have you found that you could not cope with all the things you had to do?” Summed scored range between 0 and 40 with higher scores indicating greater perceived stress ( M  = 1.94, SD = 0.37). The α reliability achieved for this sample was 0.90.

The Patient Health Questionnaire (PHQ)

The PHQ (Kroenke et al., 2010 ; Spitzer et al., 1994 ) is a well-validated measure with multiple subscales that provide provisional diagnoses for major depression (PHQ-9), generalized anxiety disorder (GAD-7), and somatization disorder, SD (PHQ-15). The scoring of these subscales included specific algorithms rather than cut-off scores to determine whether the participants met the criteria for the provisional diagnosis (see Spitzer et al., 1999 for scoring information). For this sample, the α reliability for the PHQ-9 summed score was 0.90 ( M  = 1.02, SD = 0.45) and the α reliability for the GAD-7 summed score was 0.84 ( M  = 0.98, SD = 0.20).

The UCLA Loneliness Scale

The UCLA Loneliness Scale (Russell et al., 1978 ) is a 20-item measure that assesses subjective feelings of social isolation and loneliness using a 4-point Likert scale from I often feel this way to I never feel this way , with higher summed scores indicating more loneliness ( M  = 2.25, SD = 0.27). An example item from this scale is “How often do you feel left out?” The α reliability achieved for this sample was 0.94.

The World Health Organizational Quality of Life (WHOQOL-BREF)

The WHOQOL Group (1998) developed the WHOQOL-BREF scale which assesses an individual’s perception of their quality of life during the past 2 weeks based on four distinct areas: physical health, psychological health, social relationships, and environment. This scale uses 26 items, and the raw scores are transformed to a scale ranging from 0 to 100 with the higher scores indicative of better quality of life (see WHOQOL, INT, 1996 for scoring information). For this sample, the α reliability of the unadjusted composite score is 0.91 ( M  = 3.50, SD = 0.39). For the subscales of the WHOQOL-BREF, the α reliabilities are 0.75 for physical health ( M  = 3.76, SD = 0.45), 0.85 for psychological health ( M  = 3.23, SD = 0.14), 0.73 for social relationships ( M  = 3.29; SD = 0.37), and 0.63 for environmental health ( M  = 3.57, SD = 0.31).

The Interpersonal Reactivity Index (IRI)

The IRI (Davis, 1983 ) is a well-established measure of empathy. For this study, two subscales of the IRI were included: Personal Distress and Empathic Concern, both containing seven items that used a 5-point Likert scale with responses ranging from Does not describe me well to Describes me very well such that higher summed scores on each scale indicate greater levels of empathy. The Personal Distress subscale measures apprehension and anxiety in stressed settings ( M  = 4.17, SD = 0.28). The Empathic Concern subscale assesses feelings of sympathy and concern for others considered less fortunate ( M  = 2.39, SD = 0.59). The α reliability achieved for this sample for the Personal Distress subscale was 0.76, and for the Empathic Concern subscale was 0.84.

The Checklist of Individual Strength (CIS)

The CIS (Vercoulen et al., 1999 ) is a 20-item subjective measure of general fatigue. This measure uses a 7-point Likert scale ranging from Yes, that is true of me to No, that is not true of me for each of the items. The CIS contains four subscales: fatigue, motivation, physical activity, and concentration. An example item from the concentration subscale is, “Thinking requires effort.” Higher summed scores on each subscale of the CIS indicate greater levels of fatigue. For this sample, the total summed score ( M  = 4.27, SD = 0.57) achieved an α reliability of 0.94. The fatigue subscale ( M  = 4.62, SD = 0.61) achieved an α reliability of 0.90. The motivation subscale ( M  = 3.97, SD = 0.46) achieved an α reliability of 0.75. The physical activity subscale ( M  = 4.03, SD = 0.20) achieved an α reliability of 0.87. And the concentration subscale ( M  = 4.08, SD = 0.54) achieved an α reliability of 0.89.

Pandemic-Specific Questionnaires (Created Specifically for This Study)

Concerns about pandemic.

There were 21 specific concerns about the pandemic developed by the senior author in conjunction with a larger pandemic study. Participants were asked to indicate the degree of their concern from 0 to 10 with higher scores indicating greater concern using a visual analog sliding scale. Examples of concerns listed include: Access to Food; Acquiring COVID (self/household); and Case Counts of COVID Reported. A principal components analysis with a varimax rotation was conducted to reduce the 21 items to 6 components with eigenvalues greater than one, accounting for 68.7% of the cumulative variance. The six components generated included concerns about access to basic needs, infection rates and statistics regarding COVID-19, employment and finances, childcare and schooling of underaged children, caring for or unable to visit elderly parents, and the government’s response to the pandemic. The α reliability achieved for each concern factor in this sample are as follows: Access to Basic Needs, a  = .823 ( M  = 4.85, SD = 2.92); COVID (infections and statistics), a  = .878 ( M  = 6.69, SD = 2.94); Employment and Finances, a  = .810 ( M  = 4.63, SD = 3.52); Children, a  = .885 ( M  = 2.64, SD = 3.39); Elderly Parents, a  = .741 ( M  = 4.99, SD = 3.80); and Government Response, a  = .872 ( M  = 7.11, SD = 2.47).

Behavioral and Substance Use Changes

Participants were also asked to respond to a series of questions regarding their relative change in behaviors during the initial pandemic stay-at-home recommendations. The general behaviors assessed included sleep, accessing news, alcohol use, marijuana use, anti-anxiety medication use, and sleep aid use. The participants were asked to respond if their behaviors had increased, decreased, or stayed the same from before the pandemic compared to the initial onset of the pandemic (April, 2020).

Statistical Analysis

The data were weighted to the U.S. population using four age strata, two sex strata, and four race/ethnicity strata based on the 2018 U.S. Census Bureau population estimates (2020). Cluster values were assigned to each participant based on the first two digits of the zip code provided which allowed for geographic clustering. Complex Sample Designs were used for analyses adjusting for weighting, strata, and clustering, and linear regression was conducted for comparisons of continuous variables and χ 2 tests of Independence were conducted for categorical comparisons. For analyses of continuous variables, means and standard errors are provided, and for analyses of categorical variables, percentages with 95% confidence intervals are provided. Pairwise deletion was used for random missing data. Post hoc comparisons are conducted by comparing the point estimates to the corresponding confidence intervals. Effect size comparisons are reported as Contingency Coefficients (CC) for categorical comparisons and f 2 for comparisons of continuous variables. Significance levels were set at p  < .05 for all comparisons. All analyses were conducted using SPSS version 27 (IBM, Chicago, IL).

The demographic comparisons between the four generational groups including post hoc comparisons and effect sizes are presented in Table ​ Table1. 1 . No significant differences were identified between the comparison groups when assessing gender, race/ethnicity, and education levels (all p  > .05). A higher proportion of the Gen X group were married compared with Millennial and Gen Z groups ( p  < .001). Gen Xers had a higher proportion of individuals who were divorced/separated/widowed than Millennials ( p  < .001) and a lower proportion of individuals with this marital status compared to Baby Boomers ( p  < .001). As expected, when comparing generations, there were significant differences in households with children under the age of 18, such that the there was a significantly smaller proportion of Baby Boomers households with young children ( p  < .001) and Gen X households had the highest proportion of households with underage children ( p  < .001). Likewise, employment status differed significantly between the generations such that a higher proportion of Baby Boomers were more likely to be retired and a higher proportion of Gen Zers were more likely to be students. When assessing differences in employment status, Millennials reported the highest proportion of unemployment attributed to the pandemic and the highest proportion of unemployment that was not attributed to the pandemic ( p  < .001).

Demographic comparisons

Comparisons with p  < .05 are indicated with bold font and effect sizes are provided

Values reported are column percentages and 95% confidence intervals using population weights

Post hoc comparisons use alphabetical superscripts to denote significant group differences. The superscripts for each parameter indicate the specific groups that differed significantly from the designated group, with a = Gen Z, b = Millennial, c = Gen X, and d = Baby Boomer

Psychosocial factors were also compared between the four generational groups and are shown in detail, including post hoc comparisons and effect sizes, in Table ​ Table2. 2 . Overall, individuals in the Gen Z and Millennial groups self-reported more negative outcomes for perceived stress, loneliness, the personal distress empathy subscale of the IRI, and all of the subscales of the CIS, which measure fatigue, motivation, physical activity, and concentration (all p s < .05). Most notably, the provisional rates of diagnosis for MDD for individuals in the Gen Z (44.5%) and Millennial (35.8%) groups were significantly greater than participants in the Gen X (19.2%) and Baby Boomer (11.8%) groups, which also exceed the 12-month general prevalence estimate of 10.4% prior to the pandemic (Hasin et al., 2018 ). Likewise, the 12-month general prevalence rates of GAD in the U.S. ranges between 2 and 4% (Kessler et al., 2005 ; Robichaud et al., 2019 ), and the rates of provisional diagnoses of GAD for individuals in the Gen Z (30.9%), Millennial (27.9%), and Gen X (17.2%) groups were significantly higher than those in the Baby Boomer group (8.1%).

Psychosocial measures

Reported as mean (standard error) or column percentages with 95% confidence interval using population weights

Perceived Stress Scale: higher scores = more stress

UCLA Loneliness Scale: higher scores = more lonely

WHOQOL: higher scores = better quality of life

Empathy (IRI) Empathic Concern subscale: higher scores = more empathy for others

Empathy (IRI) Personal Distress: higher scores = more distress when others are distressed

Checklist of Individual Strength: higher scores indicate worse outcomes on each subscale

When comparing the generations based on their concerns about the pandemic, significant differences were identified in three areas (see Table ​ Table3). 3 ). Millennials and Gen Xers expressed significantly higher rates of concern regarding Employment and Finances ( p  = .044), issues regarding Children ( p  = .042), and issues regarding Elderly Parents ( p  = .046) compared to the Gen Z and Baby Boomer groups. Overall, the highest levels of concerns from all of the generation groups were identified in the following two components: COVID (infected/statistics) and Government’s Response.

Concerns about pandemic

Reported as mean (standard error) using population weights

Lastly, when comparing generational groups, there were significant differences based on behavioral and substance use changes during the initial part of the pandemic (see Table ​ Table4). 4 ). When evaluating changes in sleep behaviors, 40–50% of the individuals in the Gen X, Millennial, and Gen Z groups reported decreases in sleep during the initial pandemic ( p  = .010). There was also a notable significant increase in alcohol use for individuals in the Millennial (52.2%) and Gen Z (48.5%) groups, compared to the Baby Boomer group (19.3%), with no difference between the Gen X (38.7%) and Baby Boomers (19.3%), for those who indicated prior use of alcohol ( p  = .029). For those who reported sleep aid use, there was a significant difference in the increase in use, with Gen Z (63.9%) and Millennials (62.9%) reporting higher increases during the pandemic compared to the increases reported by the Gen X (47.7%) and Baby Boomer (25.1%) groups ( p  = .041).

Changes in behaviors and substance use during initial pandemic protocols

Reported as Column percentages and 95% Confidence Intervals provided using population weights

This study examined how the COVID-19 pandemic impacted general stress levels, mental health, and maladaptive coping, including substance use, among the U.S. population across four distinct generational groups. Following a lifespan developmental perspective on stress and coping, the results of the study affirm previous research on the developmental progression of stress, coping and impacts to mental health (Aldwin, 2011 ). Given that the study used a cross-sectional research design, it is important to note that interpretation of results potentially reflects both age-related and cohort-based influences which will be further addressed in the discussion. Despite a possible combination of these two developmental influences, interpretation of the results remains noteworthy as more generational groups encounter future traumatic stressor events.

Our results showed that the younger generations (i.e., Millennials and Gen Zers) reported a greater increase in mental health symptoms when compared to Gen Xers and Baby Boomers, even though older adults are considered a higher “at-risk” group for health complications and/or hospitalization for COVID-19 infection. Specifically, we found that Millennials and Gen Zers have higher rates of MDD and GAD. Gen Xers and Baby Boomer groups showed little increase in rates of these disorders. These results are consistent with studies that have found that psychopathology symptoms were generally higher among younger generations compared to older generations (Brotto et al., 2021 ; Bruine de Bruin, 2021 ; El-Gabalaway et al., 2021; Twenge et al., 2019 ). Prior work using an age–period–cohort analysis found that Millennial and Gen Z birth cohort groups have increased rates of psychological distress and suicide-related outcomes compared with Gen X and Boomer groups independent of overall age effects (Twenge et al., 2019 ). While similar effects were found in our study, the impact of the COVID-19 pandemic on mental health may be due to a combination of age and cohort effects. In general, younger adults experience higher levels of stress and poorer mental health (i.e., anxiety and depressive symptoms), compared to older adults, such as those in the Baby Boomer group and older (American Psychological Association, 2018; Twenge et al., 2019 ). Alternatively, while older adults (i.e., 65 and older) are dealing with diminishing health and social networks, they characteristically have fewer competing responsibilities and experience more emotional well-being compared to younger adults (Momtaz et al., 2014 ). In addition, older adults may be less vulnerable to psychopathology symptoms due to normalization of negative events, and resiliency through lived experiences and accumulated wisdom (Birditt et al., 2021 ; Jeon & Dunkle, 2009 ). In fact, recent research shows that older adults have been more resilient with managing COVID-19 pandemic mental health concerns and maladaptive coping behaviors than younger age groups (Birditt et al., 2021 ). Therefore, it is noteworthy regarding the differential impact of traumatic stressor events and potential for collective trauma for differing age groups. As with previous research on stress and coping processes across the lifespan, the older the generational group the more positive outcome for mental health and less susceptibility to psychopathology and maladaptive coping.

Moreover, additional self-reported measures on pandemic concerns revealed similar generational variations. Pandemic concerns included access to basic needs, contracting viral infection, employment and finances, childcare, schooling, caring for aging parents or the inability to visit and monitor aging parents, and the government's response to the pandemic. While several of these concerns align more closely to the life circumstances of specific generational groups (i.e., having children at home), it is important to examine the intensity of the concerns to better understand how the pandemic specifically affected the individuals in different stages of life. Overall, Millennials and Gen Xers had significantly higher concerns about employment and finances, children (childcare and schooling), and caring for or visiting aging parents when compared to Gen Z and Baby Boomer generational groups.

Alcohol consumption varied among generational groups, and the findings indicated that alcohol use increased among Gen Z and Millennial participants relative to Baby Boomers. This is notable since Gen Z, the younger generational cohort known for choosing to abstain from alcohol, showed decreased rates of alcohol use compared to other birth cohorts before the pandemic (Twenge & Park, 2019 ). While it is difficult to pinpoint if the pattern of change is due age or cohort influences, given the traumatic stressor events associated with the pandemic, the Gen Z cohort group likely used alcohol as an additional coping strategy. Our results could also reflect previous research that demonstrates a developmental trajectory with older adults characteristically having matured out of risk-taking behaviors with age compared to younger adults (Josef et al., 2016 ). Studies among younger adults identified maladaptive coping as a mediator between alcohol misuse and stress (Metzger et al., 2017 ; Wang et al., 2021a , 2021b ). Consistent with a stress-coping framework, younger generational groups may use alcohol as a maladaptive coping strategy. The Gen Z birth cohort has been identified as characteristically having poorer mental health compared to other generational groups, which has been attributed to socio-historical cohort effects (American Psychological Association, 2018; Twenge et al., 2019 ). Considering the pandemic as a traumatic event created additional contextual, psychological stressors contributing to engagement in risky behaviors including drinking as a coping mechanism. Research shows that most individuals replace maladaptive coping strategies with adaptive and problem-focused coping strategies in middle and older adulthood age groups (Al-Bahrani et al., 2013 ; Diehl et al., 1996 ). Such patterns of change in coping may be due to traumatic stressors associated with the COVID-19 pandemic (Brotto et al., 2021 ). Future research studies utilizing cross-sequential designs to examine associations between generational cohort groups with a focus on psychopathology and mental health vary as a function of maladaptive coping would be an important next step.

Last, while sleep problems were a common concern prior to the pandemic, there were significant issues related to sleep disturbance at the onset of the pandemic. The Gen X, Gen Z, and Millennial groups experienced poor quality or insufficient sleep which can impact health and well-being (Clement-Carbonell et al., 2021 ). There is evidence that the Gen Z group was prone to decreased sleep duration before the pandemic, potentially due to increased time devoted to electronic media use (Twenge et al., 2017 ). Given the established research on quality sleep and improved mental health, it is important to reframe quality sleep as an integral aspect of supporting mental health, especially at the onset of traumatic stressor events (Scott et al., 2021 ). Interestingly, while Gen X, Gen Z, and Millennial groups reported issues related to sleep, only Gen Z and Millennial groups showed significant increased use of sleep aids. This suggests that younger generational groups were more inclined to use a sleep aid, while Gen X participants also struggled with sleep yet were less likely to use a sleep aid. Previous research on sleep medication and mental health indicates that individuals meeting 2-week provisional MDD, SD, and GAD diagnoses were more likely to use a sleep aid (Grigsby et al., 2022 ). Therefore, while it is important for everyone to be screened for sleep issues and/or the use of sleep aids, potential recommendations and interventions may differ across generational groups. It may be particularly prudent to address these issues with the Gen Z group, since these behaviors can be contributing factors to poor mental health outcomes during times of traumatic stress.

Limitations

This study has several limitations. First, a population-based, cross-sectional research design was used to expedite data collection during the initial stages of the pandemic. We were interested in examining the different generational cohort responses to the COVID-19 pandemic as birth cohort identification has become of a focus of popular media and led to an increase in identification with these labels. Since the COVID-19 pandemic has had a relatively unique impact as an immediate and now chronic stressor, it is difficult to definitively separate out the contribution of aging from that of generational cohort effects on mental health and maladaptive coping behaviors related to the pandemic. Furthermore, while cross-sectional research designs allow for timely data collection, the downside to this methodology is separating out age-related changes from socio-historical cohort effects across age groups that can confound results. To avoid such limitations, future research mental health and generational cohorts should use longitudinal or cross-sequential timepoints to better characterize the impacts of the COVID-19 pandemic that are related to age or cohort influences. Second, the demographic makeup of the sample recruited was predominately non-Hispanic White and female. We attempted to ameliorate the lack of generalizability to the U.S. population by employing statistical techniques which included weights and clustering. Next, it is difficult to derive causal relationships between generational differences in mental health outcomes, substance use, and coping strategies, so interpreting associations between variables should be done carefully. Further, because the items assessing substance use behaviors were ordinal (i.e., decreased, increased), it is not possible to quantify the changes in behaviors, which would have provided greater insight to problematic alcohol and substance use. Lastly, data were collected using a targeted ad campaign on social media and may be impacted by selection and response bias. By doing so, there were differences in the comparison group sizes, with the Gen Z group representing 3.2% of the general sample, and 8.8% of the weighted sample. This discrepancy is likely due to only including Gen Zs who were 18 years or older and using Facebook as the recruitment tool. However, the sample was weighted to reflect the total U.S. population based on generational and age estimates to mitigate this limitation. Further, while social media use is highly prevalent among all adult age groups in the U.S., it is possible that older adults who are less likely to use social media or technology may have been more vulnerable during the pandemic (Hajek & König, 2021 ).

These preliminary findings highlight the importance of conducting future research investigating the implementation of early intervention strategies (e.g., early screening and detection) and access to mental health resources for younger adults during the initial outbreak of a pandemic. While everyone can be affected by a global pandemic or other traumatic stressor events, developmentally, some will experience a stronger, more salient impact than others. Our results indicate that younger adults belonging to Gen Z were a more psychologically vulnerable population compared to older adults belonging to the baby boomer cohort who demonstrate more resiliency in mental health outcomes (Chen, 2020 ). Future studies should continue to explore developmental differences in psychopathology and coping behaviors between generational groups to buffer against symptoms of psychopathology. Gen Z and Millennial generations are more likely to seek out mental health resources through social media or online self-tools, so using these online platforms to screen for psychopathology through community-wide programming strategies is key. Despite similarities, even the younger generational cohorts have been found to seek out and interact differently to digital intervention materials related to substance abuse (Ashford et al., 2020 ; Curtis et al., 2019 ). Therefore, targeted, developmental-appropriate, prevention–intervention strategies should be implemented at the onset of traumatic stressor events to mitigate, maladaptive psychological antecedents which contribute to psychopathology and mental health disorders.

This research received no specific grant from any funding agency, commercial, or not-for-profit sectors.

Declarations

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Ahmed MZ, Ahmed O, Aibao Z, Hanbin S, Siyu L, Ahmad A. Epidemic of COVID-19 in China and associated psychological problems. Asian Journal of Psychiatry. 2020; 51 :102092. doi: 10.1016/j.ajp.2020.102092. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Al-Bahrani M, Aldhafri S, Alkharusi H, Kazem A, Alzubiadi A. Age and gender differences in coping style across various problems: Omani adolescents’ perspective. Journal of Adolescence. 2013; 36 (2):303–309. doi: 10.1016/j.adolescence.2012.11.007. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Aldwin C. Stress and coping across the lifespan. In: Folkman S, editor. The Oxford handbook of stress, health, and coping. Oxford University Press; 2011. pp. 15–34. [ Google Scholar ]
• American Psychological Association. (2018, October). Stress in America: Generation Z . https://www.apa.org/news/press/releases/stress/2018/stress-gen-z.pdf
• Ames-Guerrero RJ, Barreda-Parra VA, Huamani-Cahua JC, Banaszak-Holl J. Self-reported psychological problems and coping strategies: A web-based study in Peruvian population during COVID-19 pandemic. BMC Psychiatry. 2021; 21 (1):351. doi: 10.1186/s12888-021-03326-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Anjum S, Ullah R, Rana MS, Khan HA, Memon FS, Ahmed Y, Jabeen S, Faryal R. COVID-19 pandemic: A serious threat for public mental health globally. Psychiatria Danubina. 2020; 32 (2):245–250. doi: 10.24869/psyd.2020.245. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arnett JJ. Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist. 2000; 55 (5):469–480. doi: 10.1037/0003-066X.55.5.469. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ashford RD, Giorgi S, Mann B, Pesce C, Sherritt L, Ungar L, Curtis B. Digital Recovery networks: Characterizing user participation, engagement, and outcomes of a novel recovery social network smartphone application. Journal of Substance Abuse Treatment. 2020; 109 :50–55. doi: 10.1016/j.jsat.2019.11.005. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baltes PB. Theoretical propositions of life-span developmental psychology: On the dynamics between growth and decline. Developmental Psychology. 1987; 23 (5):611–626. doi: 10.1037/0012-1649.23.5.611. [ CrossRef ] [ Google Scholar ]
• Baltes PB, Lindenberger U, Staudinger UM. Life span theory in developmental psychology. In: Lerner RM, Damon W, editors. Handbook of child psychology: Theoretical models of human development. Wiley; 2006. pp. 569–664. [ Google Scholar ]
• Brooks SK, Webster RK, Smith LE, Woodland L, Wessely SG, Rubin GJ. The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet. 2020; 395 :912–920. doi: 10.1016/S0140-6736(20)30460-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Birditt KS, Turkelson A, Fingermann KL, Polenick CA, Oya A. Age differences in stress, life changes, and social ties during the COVID-19 pandemic: Implications for psychological well-being. The Gerontologist. 2021; 62 (2):205–216. doi: 10.1093/geront/gnaa204. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brotto LA, Chankassing K, Baaske A, Albert A, Booth A, Kaida A, et al. The influence of sex, gender, age, and ethnicity on psychosocial factors and substance use throughout phases of the COVID-19 pandemic. PLoS ONE. 2021; 16 (11):e0259676. doi: 10.1371/journal.pone.0259676. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brown MM, Arigo D. Changes in life circumstances and mental health symptoms during the COVID-19 pandemic among midlife women with elevated risk for cardiovascular disease. Journal of Women and Aging. 2022; 34 (5):637–648. doi: 10.1080/08952841.2021.1967654. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bruine de Bruin W. Age differences in COVID-19 risk perceptions and mental health: Evidence from a national US survey conducted in March 2020. The Journals of Gerontology, Series b: Psychological Sciences and Social Sciences. 2021; 76 (2):e24–29. doi: 10.1093/geronb/gbaa074. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bruno RL, Frick NM. The psychology of polio as prelude to post-polio sequelae: Behavior modification and psychotherapy. Orthopedics. 1991; 14 (11):1185–1193. doi: 10.3928/0147-7447-19911101-06. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bu F, Steptoe A, Fancourt D. Who is lonely in lockdown? Cross-cohort analyses of predictors of loneliness before and during the COVID-19 pandemic. Public Health. 2020; 186 :31–34. doi: 10.1016/j.puhe.2020.06.036. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buffel T, Yarker S, Phillipson C, Lang L, Lewis C, Doran P, Goff M. Locked down by inequality: Older people and the COVID-19 pandemic. Urban Studies. 2021 doi: 10.1177/00420980211041018. [ CrossRef ] [ Google Scholar ]
• Center for Disease Control and Prevention. (2020). Previous U.S. COVID-19 cases data . Retrieved September 2, 2021, from https://www.cdc.gov/coronavirus/2019-ncov/covid-data/previouscases.html#anchor_1598477311
• Center for Disease Control and Prevention. (2021). COVID-19 risks and vaccine information for older adults . Retrieved January 31, 2022, from https://www.cdc.gov/aging/covid19/covid19-older-adults.html#:~:text=Older%20adults%20are%20more%20likely,60s%2C%2070s%2C%20and%2080s
• Charles ST. Strength and vulnerability integration: A model of emotional well-being across adulthood. Psychological Bulletin. 2010; 136 (6):1068–1091. doi: 10.1037/a0021232. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen LK. Older adults and COVID-19 pandemic: Resilience matters. Archives of Gerontology and Geriatrics. 2020; 89 :104124. doi: 10.1016/j.archger.2020.104124. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Clement-Carbonell V, Portilla-Tamarit I, Rubio-Aparicio M, Madrid-Valero JJ. Sleep quality, mental and physical health: A differential relationship. International Journal of Environmental Research and Public Health. 2021; 18 (2):460. doi: 10.3390/ijerph18020460. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. Journal of Health and Social Behavior. 1983; 24 (4):385–396. doi: 10.2307/2136404. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cordaro M, Grigsby TJ, Howard JT, Deason RG, Haskard-Zolnierek K, Howard K. Pandemic-specific factors related to generalized anxiety disorder during the initial COVID-19 protocols in the United States. Issues in Mental Health Nursing. 2021; 42 (8):747–757. doi: 10.1080/01612840.2020.1867675. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Creswell KG. Drinking together and drinking alone: A social-contextual framework for examining risk for Alcohol Use Disorder. Current Directions in Psychological Science. 2021; 30 (1):19–25. doi: 10.1177/0963721420969406. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Curtis BL, Ashford RD, Magnuson KI, Ryan-Pettes SR. Comparison of smartphone ownership, social media use, and willingness to use digital interventions between Generation Z and Millennials in the treatment of substance use: Cross-sectional questionnaire study. Journal of Medical Internet Research. 2019; 21 (4):e13050. doi: 10.2196/1305. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Davis MH. Measuring individual differences in empathy: Evidence for a multidimensional approach. Journal of Personality and Social Psychology. 1983; 44 (1):113–126. doi: 10.1037/0022-3514.44.1.113. [ CrossRef ] [ Google Scholar ]
• Diehl M, Coyle N, Labouvie-Vief G. Age and sex differences in strategies of coping and defense across the life span. Psychology and Aging. 1996; 11 (1):127–139. doi: 10.1037/0882-7974.11.1.127. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• El-Gabalawy R, Sommer JL. “We are at risk too”: The disparate mental health impacts of the pandemic on younger generations. Canadian Journal of Psychiatry. Revue Canadienne De Psychiatrie. 2021; 66 (7):634–644. doi: 10.1177/0706743721989162. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fields EC, Kensinger EA, Garcia SM, Ford JH, Cunningham TJ. With age comes well-being: Older age associated with lower stress, negative affect, and depression throughout the COVID-19 pandemic. Aging and Mental Health. 2022; 26 (10):2071–2079. doi: 10.1080/13607863.2021.2010183. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grigsby TJ, Howard JT, Deason RG, Haskard-Zolnierek KB, Howard K. Correlates of COVID-19 pandemic-related increases in sleep aid and anti-anxiety medication use. Journal of Substance Use. 2022; 27 (1):56–61. doi: 10.1080/14659891.2021.1892221. [ CrossRef ] [ Google Scholar ]
• Hajek A, König HH. Social isolation and loneliness of older adults in times of the COVID-19 pandemic: Can use of online social media sites and video chats assist in mitigating social isolation and loneliness? Gerontology. 2021; 67 (1):121–124. doi: 10.1159/000512793. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, Grant BF. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry. 2018; 75 (4):336–346. doi: 10.1001/jamapsychiatry.2017.4602. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hirschberger G. Collective Trauma and the social construction of meaning. Frontiers in Psychology. 2018; 9 :1441. doi: 10.3389/fpsyg.2018.01441. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hossain MM, Tasnim S, Sultana A, Faizah F, Mazumder H, Zou L, McKyer E, Ahmed HU, Ma P. Epidemiology of mental health problems in COVID-19: A review. F1000Research. 2020; 9 :636. doi: 10.12688/f1000research.24457.1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Howard K, Grigsby TJ, Haskard-Zolnierek KB, Deason R, Howard JT. Pandemic-related work status is associated with self-reported increases in substance use. Journal of Workplace Behavioral Health. 2021; 36 (3):250–257. doi: 10.1080/15555240.2021.1941067. [ CrossRef ] [ Google Scholar ]
• Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: A web-based cross-sectional survey. Psychiatry Research. 2020; 288 :112954. doi: 10.1016/j.psychres.2020.112954. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jeon H, Dunkle RE. Stress and depression among the older-old: A longitudinal analysis. Research on Aging. 2009; 31 (6):661–687. doi: 10.1177/0164027509343541. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Josef AF, Richter D, Samanez-Larkin GR, Wagner GG, Hertwig R, Mata R. Stability and change in risk-taking propensity across the adult life span. Journal of Personality and Social Psychology. 2016; 111 (3):430–450. doi: 10.1037/pspp0000090. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey replication. Archives of General Psychiatry. 2005; 62 (6):617–627. doi: 10.1001/archpsyc.62.6.617. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kiss O, Alzueta E, Yuksel D, Pohl KM, de Zambotti M, Műller-Oehring EM, et al. The pandemic's toll on young adolescents: Prevention and intervention targets to preserve their mental health. The Journal of Adolescent Health. 2022; 70 (3):387–395. doi: 10.1016/j.jadohealth.2021.11.023. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klaiber P, Wen JH, DeLongis A, Sin NL. The ups and downs of daily life during COVID-19: Age differences in affect, stress, and positive events. The Journals of Gerontology Series b, Psychological Sciences and Social Sciences. 2021; 76 (2):e30–e37. doi: 10.1093/geronb/gbaa096. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kowal M, Coll-Martín T, Ikizer G, Rasmussen J, Eichel K, Studzińska A, Koszałkowska K, Karwowski M, Najmussaqib A, Pankowski D, Lieberoth A, Ahmed O. Who is the most stressed during the COVID-19 pandemic? Data from 26 countries and areas. Applied Psychology: Health and Well-Being. 2020; 12 (4):946–966. doi: 10.1111/aphw.12234. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kroenke K, Spitzer RL, Williams JBW, Löwe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. General Hospital Psychiatry. 2010; 32 (4):345–359. doi: 10.1016/j.genhosppsych.2010.03.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lind M, Bluck S, McAdams DP. More vulnerable? The life story approach highlights older people’s potential for strength during the pandemic. The Journals of Gerontology Series b, Psychological Sciences and Social Sciences. 2021; 76 (2):e45–e48. doi: 10.1093/geronb/gbaa105. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Littleton H, Horsley S, John S, Nelson DV. Trauma coping strategies and psychological distress: A meta-analysis. Journal of Traumatic Stress. 2007; 20 (6):977–988. doi: 10.1002/jts.20276. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Luo MS, Chui EWT, Li LW. The longitudinal associations between physical health and mental health among older adults. Aging and Mental Health. 2020; 24 (12):1990–1998. doi: 10.1080/13607863.2019.1655706. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Metzger IW, Blevins C, Calhoun CD, Ritchwood TD, Gilmore AK, Stewart R, Bountress KE. An examination of the impact of maladaptive coping on the association between stressor type and alcohol use in college. Journal of American College Health. 2017; 65 (8):534–541. doi: 10.1080/07448481.2017.1351445. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moghanibashi-Mansourieh A. Assessing the anxiety level of Iranian general population during COVID-19 outbreak. Asian Journal of Psychiatry. 2020; 51 :102076. doi: 10.1016/j.ajp.2020.102076. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Momtaz Y, Ibrahim R, Hamid T-A. Cohort comparisons: Emotional well-being among adolescents and older adults. Clinical Interventions in Aging. 2014 doi: 10.2147/cia.s61636. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Office of the Surgeon General (OSG). (2021). Protecting Youth Mental Health: The U.S. Surgeon General’s Advisory . US Department of Health and Human Services. https://www.hhs.gov/sites/default/files/surgeon-general-youth-mental-health-advisory.pdf [ PubMed ]
• Park CL, Finkelstein-Fox L, Russell BS, Fendrich M, Hutchison M, Becker J. Psychological resilience early in the COVID-19 pandemic: Stressors, resources, and coping strategies in a national sample of Americans. American Psychologist. 2021 doi: 10.1037/amp0000813. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• PEW Research Center. (2019). Defining Generations: Where Millennials end and Generation Z begins . https://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-and-generation-z-begins/
• Robichaud, M., Koerner, N., & Dugas, M. J. (2019). Cognitive–behavioral treatment for generalized anxiety disorder: From science to practice. Routledge/Taylor & Francis Group.
• Russell DW, Peplau LA, Ferguson ML. Developing a measure of loneliness. Journal of Personality Assessment. 1978; 42 (3):290–294. doi: 10.1207/s15327752jpa4203_11. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Salari N, Hosseinian-Far A, Jalali R, Vaisi-Raygani A, Rasoulpoor S, Mohammadi M, Rasoulpoor S, Khaledi-Paveh B. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: A systematic review and meta-analysis. Globalization and Health. 2020; 16 (1):57. doi: 10.1186/s12992-020-00589-w. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Scott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S. Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Medicine Reviews. 2021; 60 :101556. doi: 10.1016/j.smrv.2021.101556. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spiro AIII. The relevance of a lifespan developmental approach to health. In: Aldwin CM, Park CL, Spiro A, editors. Handbook of health psychology and aging. The Guilford Press; 2007. pp. 75–93. [ Google Scholar ]
• Spitzer RL, Williams JBW, Kroenke K, Linzer M, de Gruy FV, Hahn SR, Brody D, Johnson JG. Utility of a new procedure for diagnosing mental disorders in primary care: The PRIME-MD 1000 study. JAMA. 1994; 272 :1749–1756. doi: 10.1001/jama.1994.03520220043029. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spitzer RL, Kroenke K, Williams JBW. Validation and unity of a self-report version of PRIME-MD: The PHQ primary care study. JAMA. 1999; 282 (18):1737–1744. doi: 10.1001/jama.282.18.1737. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tartaglia S, Bergagna E. Alcohol consumption as a maladaptive coping strategy to face low life satisfaction. Drugs: Education, Prevention and Policy. 2019; 27 (4):306–311. doi: 10.1080/09687637.2019.1685938. [ CrossRef ] [ Google Scholar ]
• Turchioe M, Grossman LV, Myers AC, Pathak J, Creber RM. Correlates of mental health symptoms among US adults during COVID-19, March–April 2020. Public Health Reports. 2021; 136 (1):97–106. doi: 10.1177/0033354920970179. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Twenge JM, Cooper AB, Joiner TE, Duffy ME, Binau SG. Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005–2017. Journal of Abnormal Psychology. 2019; 128 (3):185–199. doi: 10.1037/abn0000410. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Twenge JM, Krizan Z, Hisler G. Decreases in self-reported sleep duration among U.S. adolescents 2009–2015 and association with new media screen time. Sleep Medicine. 2017; 39 :47–53. doi: 10.1016/j.sleep.2017.08.013. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Twenge JM, Park H. The decline in adult activities among U.S. adolescents, 1976–2016. Child Development. 2019; 90 (2):638–654. doi: 10.1111/cdev.12930. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• U.S. Census Bureau. (2018). Annual estimates of the resident population by sex, age, race, and Hispanic origin: April 1, 2010 to July 1, 2018 . https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-detail.html
• U.S. Department of Labor. (2020). Unemployment insurance weekly claims . Retrieved September 2, 2021, from https://www.dol.gov/ui/data.pdf
• Uwadiale A, Cordaro M, Brunett K, Stern M, Howard K. Lessons learned about the need for early screening for depression during the first months of the COVID-19 pandemic in the United States. Issues in Mental Health Nursing. 2021 doi: 10.1080/01612840.2021.1975333. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vercoulen JHMM, Alberts M, Blejenberg G. The checklist of individual strength (CIS) Gedragstherapie. 1999; 32 :131–136. [ Google Scholar ]
• Wang CD, Anderson-White E, Jin L. Adult attachment, maladaptive coping, alcohol consumption, and drinking-related problems. Counselling Psychology Quarterly. 2021 doi: 10.1080/09515070.2021.1977914. [ CrossRef ] [ Google Scholar ]
• Wang Y, Di Y, Ye J, Wei W. Study on the public psychological states and its related factors during the outbreak of coronavirus disease 2019 (COVID-19) in some regions of China. Psychology, Health and Medicine. 2021; 26 (1):13–22. doi: 10.1080/13548506.2020.1746817. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wettstein M, Wahl HW, Schlomann A. The impact of the COVID-19 pandemic on trajectories of well-being of middle-aged and older adults: A multidimensional and multidirectional perspective. Journal of Happiness Studies. 2022; 23 (7):3577–3604. doi: 10.1007/s10902-022-00552-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• WHOQOL, INT. (1996). WHOQOL-BREF introduction, administration, scoring and generic version of the assessment: Field trial version . https://www.who.int/mental_health/media/en/76.pdf?ua=1
• WHOQOL-Group Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychological Medicine. 1998; 28 (3):551–558. doi: 10.1017/S0033291798006667. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wilton R, et al. Geographies of (domestic alcohol consumption) In: Andrews GJ, et al., editors. COVID-19 and similar futures, Global perspectives on health geography. Springer; 2021. pp. 387–392. [ Google Scholar ]
• World Health Organization. (2020, March). Mental health and psychosocial considerations during the COVID-19 outbreak . WHO. https://www.who.int/docs/default-source/coronaviruse/mental-health-considerations
• Xiong J, Lipsitz O, Nasri F, Lui L, Gill H, Phan L, Chen-Li D, Iacobucci M, Ho R, Majeed A, McIntyre RS. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. Journal of Affective Disorders. 2020; 277 :55–64. doi: 10.1016/j.jad.2020.08.001. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

The Psychology Behind Generational Conflict

Older people have groused about younger people for millennia. Now we know why

Ted Scheinman

Senior Editor

Complaining about the young is a longstanding prerogative of the old; just as baby boomers and Gen X’ers today lament the shortcomings of millennials and Gen Z, parents in the 1920s looked askance at their flapper daughters, the mothers of pre-revolutionary France pooh-poohed their “effeminate” sons, and so on back to the fourth century B.C. and Aristotle, who said of Greece’s young people: “They think they know everything, and are always quite sure about it.”

Now, some 2,500 years later, researchers are offering a pair of psychological explanations for this recurring complaint, or what they call the “kids these days effect.” In studies involving 3,458 Americans ages 33 to 51 recruited and evaluated online, John Protzko and Jonathan Schooler of the University of California, Santa Barbara, measured respondents’ authoritarian tendencies, intelligence and enthusiasm for reading. “While people may believe in a general decline,” the researchers observed in the journal Science Advances , “they also believe that children are especially deficient on the traits in which they happen to excel.”

Authoritarian people, it turns out, are more likely to suspect that today’s youth are lacking in respect for authority, while well-read people are more likely to bemoan that kids these days never seem to be reading. More intelligent people are also more likely to say that young people are getting stupider—a remarkable conviction, given decades of rising intelligence domestically and globally.

At the heart of this denigrating effect is flawed memory, Protzko and Schooler say. Sometimes older people mistakenly recall that kids in the past were more accomplished than today’s kids, who suffer by comparison. “People in their 20s and 30s are going to grow up looking at kids and thinking they’re deficient,” Protzko says. So, while the baby boomers continue to weather volleys of “OK, boomer” from youngsters who blame them for despoiling the earth, older Americans can take comfort in knowing that members of Generation Z will one day hear the inevitable: “OK, zoomer.”

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Ted Scheinman is a senior editor for Smithsonian magazine. He is the author of Camp Austen: My Life as an Accidental Jane Austen Superfan .

Why We Shouldn't Exaggerate Generational Differences

Lumping any group of people into a designated bucket is misguided..

Posted May 3, 2021 | Reviewed by Matt Huston

• Commonly accepted generational differences are largely a myth.
• Creating and perpetuating such alleged differences can do real damage to people's lives.
• Focusing on commonalities versus differences will go a long way to creating a more equitable and just society.

We’ve heard them over and over. Pithy nuggets of alleged wisdom describing the collective attributes of a generation and the individual characteristics of their members. They generally go as follows:

• Silent Generation (born 1927-1946): Having grown up during the Great Depression or World War II, members of the Silent Generation are loyal and dependable, a product of their yearning for stability;
• Baby Boomers (born 1946-1964): Boomers are entitled and self-centered, a result of the postwar obsession with children and their good fortune of being raised during an economic boom;
• Generation X (born 1965-1980): Gen Xers are angry and deservedly so, caught in the wake of boomers’ huge numbers and their financial windfall;
• Millennials (born 1981-1995): As digital natives, Millennials rule the social media roost and avoid deep professional and personal commitments like the plague, preferring to focus on the now and keep their career and relationship options open;
• Generation Z (born 1997-2012ish): Zers are conservative and risk-averse, a throwback of sorts to the no-drama-please ways of the Silent Generation.

Is there any truth to such generational generalizations? Perhaps, as it can be seen how the seminal events that define a certain period of time may make a lasting impression on an individual, especially during one’s youth. I was a teenager during the counterculture, for example, and watching the events of the late 1960s and early 1970s unfold (Vietnam, assassinations, energy crisis, airplane hijackings, and Watergate, to name just a few of that era’s greatest hits) convinced me that the world is a chaotic place where pretty much anything can happen anytime. That perspective has served me and many of my fellow boomers well, however, as it laid an experiential foundation by which to place the pandemic in historical context.

Overall, however, the creation and perpetuation of such generational archetypes is not just a silly exercise but a potentially harmful one, especially in the workplace. Lumping any group of people into a designated bucket is misguided, for one thing, especially when tens of millions of unique individuals are involved.

The problem is that defining humans by any single attribute is a dangerous business. Discrimination and hate can result from such seemingly harmless “otherness,” fueling a more competitive and Darwinian society. Making suppositions or conclusions about a group of people based on the year they happened to have been born is something that should be permanently retired, like racism and misogyny.

Rather than continue such a divisive practice, we should instead focus on the much more obvious commonalities that humans share in order to build bridges across generations and all other socially constructed barriers. In a previous post, I proposed that there are 10 traits that we share as a species. Concentrating on these instead of our alleged differences could go a long way toward creating a more equitable and just society.

These traits are, in no particular order:

1. Belonging. We’re all social beings, meaning we rely on meaningful relationships with others.

2. Community. Likewise, we have a longing to be part of something bigger than our individual selves.

3. Creativity . Humans share the drive to use their imaginations to make something that previously didn’t exist.

4. Curiosity. We are inquisitive organisms with a desire to figure out what makes things tick.

5. Family. The desire for kinship, biologically based or otherwise, is hard-wired into our genetic makeup.

6. Love. This strong, chemically induced emotional state is nature’s trick for getting us to perpetuate the species.

7. Memory . Our brains are receptacles of the past, a means of passing on our life stories to the next generation for continuity.

8. Purpose. Each of us is here for a reason, and our mission is to find out what that is and then do it as best we can.

9. Storytelling. Chronicling some aspect of the human condition in some way to someone else is our primary form of communication.

10. Voice. All of us have the need to express ourselves in a unique way to tell the rest of the world who we are.

These 10 human traits transcend all the superficial differences we spend way too much time thinking and talking about. Let’s use them as ways to bring us closer together, even as we acknowledge and respect our wonderful diversity.

Lawrence R. Samuel, Ph.D. , is an American cultural historian who holds a Ph.D. in American Studies and was a Smithsonian Institution Fellow.

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• Published: 06 July 2023

Generational differences in climate-related beliefs, risk perceptions and emotions in the UK

• Wouter Poortinga   ORCID: orcid.org/0000-0002-6926-8545 1 , 2 ,
• Christina Demski   ORCID: orcid.org/0000-0002-9215-452X 3 &
• Katharine Steentjes   ORCID: orcid.org/0000-0002-8661-8287 1  

Communications Earth & Environment volume  4 , Article number:  229 ( 2023 ) Cite this article

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• Climate change
• Climate-change policy
• Psychology and behaviour

It is widely believed that younger generations are more engaged with climate change than older generations. However, evidence of a gap in climate-related perceptions and concern is mixed, likely due to the inconsistent use of outcome variables. Here we systematically examine generational differences across different types of climate engagement including cognitive and affective dimensions. Using data from three nationally-representative surveys conducted in the UK in 2020, 2021 and 2022, we show there is an overall pattern of higher levels of climate-related beliefs, risks perceptions and emotions among younger generation groups. However, the gap is larger and more consistent for climate-related emotions than for climate-related beliefs. While generational differences in climate-related emotions were found across all years, the overall gap has disappeared due to narrowing climate-related beliefs and risk perceptions. The generational differences are therefore mainly in emotional engagement rather than in beliefs about anthropogenic climate change.

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Climate change affects multiple dimensions of well-being through impacts, information and policy responses

The case for longtermism: concern for the far future as a catalyst for pro-climate action

Ten-year panel data confirm generation gap but climate beliefs increase at similar rates across ages

Introduction.

Research often shows that younger age groups are more concerned about climate change than older age groups 1 , 2 , 3 . This appears intuitive, given that younger age groups have grown up earlier hearing and learning about climate change and will be affected more by its consequences. The increased level of concern even goes beyond emotionally manageable levels as recent literature suggests. Evidence is growing that climate related anxiety is taking a toll on the wellbeing of children and young people, as they become aware of the threats posed by a heating planet 4 , 5 , 6 , 7 . The idea that there is a generation gap in engagement with climate change is further strengthened by young climate activists capturing the media’s and public’s attention 8 , 9 .

There are however questions regarding the nature and the size of the generation gap, as effects have not been observed consistently. Age-related differences have been found in beliefs about the reality, causes, and impacts of climate change, with older individuals being more likely to express climate sceptical views than younger ones 3 , 10 , 11 , 12 ; and there is evidence that younger people are more concerned about the environment in general 13 , 14 and climate change in particular 2 , 15 . Furthermore, younger age groups may be more likely to experience climate-related emotions, such as worry, anger and guilt 16 , as well as climate-related anxiety 4 . However, other research only found small or absent age differences 17 . For example, Shi and colleagues (2016) report that age was not significant in explaining climate concern in five out of six countries; and a meta-analysis of research published between 1970 and 2010 concluded that age effects for environmental concern, values and commitment were negligible 18 .

The mixed evidence regarding the nature and size of the generational gap may in part be due to methodological differences, and specifically because of the inconsistent use of outcome measures that suggest a similar comparison when they indeed express different levels of cognitive and affective engagement with the issue. Common variables used in previous studies include climate change beliefs, which are propositional cognitions about the nature of climate change that may or may not correspond with reality, and climate concern, which reflects an emotional state resulting from an affective evaluation of the seriousness of the impacts of climate change 3 . Risk perceptions as a related but different construct can be subdivided into perceived likelihood and seriousness, generalised concern, and personal worry 19 . Where perceived likelihood and seriousness are subjective cognitive evaluations of the risks and impacts of climate change, personal worry reflects a more experiential, emotional response to an uncertain and potentially dangerous future caused by the issue. Generalised concern is similarly experiential, but in contrast to worry can be expressed without motivational or emotional content 19 . That is, someone who is concerned about climate change may consider it a serious issue without experiencing feelings of tension or unease. In turn, worry and concern can be distinguished from the more intense emotions of fear 20 , 21 , 22 , which is related to the fight-or-flight defence system 23 , and anxiety 4 , 6 , which is characterized by excessive and uncontrollable apprehension that can lead to psychological distress and physical symptoms. As such, climate fear and anxiety are potentially more debilitating and maladaptive than climate concern and worry 6 , 24 . Fig.  1 represents how the different constructs of climate-related beliefs, risk perceptions and emotions can be conceptually related to one another. This comprehensive model of climate engagement holds that the different cognitive and affective components of climate-related perceptions reflect different types and degrees of engagement with climate change, comprising cognitions about its nature (beliefs), subjective evaluation of its risks and consequences (risk perceptions), and the feelings it evokes (emotions). That is, the different climate-related components can be placed on a cognitive-affective dimensions, with the more affective constructs reflecting a higher level of engagement with the issue. As suggested by Fig.  1 , there is a hierarchical relationship between climate-related beliefs, risk perceptions and emotions. The lower components of the model are a necessary but insufficient condition for the higher components. While someone can recognise the reality of anthropogenic climate change, the person may not perceive it as a threat or experience any climate related emotions. On the other hand, in order to experience climate-related emotions, one has to believe that anthropogenic climate change is real and poses a threat. As such, it is important to clearly distinguish between the different components, as generational differences may exist for some but not for others.

Comprehensive model of climate engagement.

Here, we explore generational differences across all these dimensions of climate engagement, using data from three cross-sectional nationally-representative surveys conducted in 2020, 2021 and 2022 in the United Kingdom (UK). The surveys contained a range of questions on beliefs regarding the causes, temporal proximity and urgency of climate change, as well as perceived impacts and threats, worry and other experienced emotions. The three cross-sectional surveys were analysed independently using the named groups based on the theory of generations, whereby different generations are shaped by shared experiences based on specific social and historical events and circumstances 25 , 26 . These generational labels help to draw together insights about different age cohorts over time, based on the assumption that those shared experiences lead to the formation of common values and opinions amongst the individuals 27 . The six named generations relevant to the analysis are the Post-War (or ‘silent’) generation (born between 1928 and 1945), the first half of the baby boomer generation (born between 1946 and 1954), the second half of the baby boomer generation (born between 1955 and 1964), Generation X (born between 1965 and 1980), Millennials (born between 1981 and 1996), and Generation Z (born after 1996). We use these named groups, as it is the most widely-used classification of generations in the western world and as a result are widely recognised. Furthermore, (media) reports and several recent academic studies have used this classification to discuss generational differences in relation to climate change 16 , 28 , 29 .

Generational differences in climate-related beliefs, risk perceptions and emotions

Participants in the surveys responded to ten questions to assess their beliefs, risk perceptions and experienced emotions regarding climate change. Most questions used a 5-point response scale, with higher scores indicating higher levels of belief in the anthropogenic nature, temporal proximity and urgency of climate change, higher levels of perceived risks and threats, and more strongly felt emotions. The temporal proximity question (“already feeling the effects”) was dichotomized due to the distribution of scores (67%, 65% and 68% indicated that they think we are already feeling the effects of climate change in 2020, 2021 and 2022 respectively).

Table  1 shows the mean scores and standard deviations for the ten questions for the five generation groups in 2020, 2021, and 2022 respectively. There is an overall pattern of higher levels of climate-related beliefs, risk perceptions and emotions among the younger generation groups, in particular in 2020. While the differences between the generation groups appear less profound in 2021 and 2022, with Generation Z and Millennials having slightly lower scores and the Boomers I and Post war group having slightly higher scores than in 2020, the overall pattern is the same.

Linear and logistic regression analyses of the responses show that there were no significant differences in the perceived causes and urgency of climate change across the five generation groups in 2020 (see Table  2 ). The Boomer II group are however more than two-and-a-half times more likely than the Generation Z group to think “we are already feeling the effects” of climate change (OR = 2.70, 95%CI [1.24, 5.84]). While there were no significant differences in the perceived impacts of climate change across the generation groups, the perceived threats to self and family and to the UK were lower for all generation groups as compared to Generation Z. Similarly, the Generation X, Boomer II, and Boomers I and older groups were less worried, and less strongly felt the emotions of fear, guilt and outrage. Overall, these results suggests that, while there are no major differences in climate-related beliefs, there may be a generation gap in climate-related risk perceptions and emotions.

Table  2 further shows there were fewer significant differences between the generations in 2021 and 2022. In contrast to the results from 2020, the Boomers II and Boomers I and older groups show a higher belief in the anthropogenic nature of climate change as compared to Generation Z in 2021. However, this difference is absent again in 2022. The two groups (and Millennials in 2022) also had a higher level of belief that we are already feeling the effects of climate change compared to Generation Z in both years (OR = 2.30, 95%CI [1.15, 4.72] and OR = 4.66, 95%CI [2.40, 9.04] for the Boomers II group in 2021 and 2022, respectively; OR = 4.04, 95%CI [2.00, 8.19] and OR = 3.42, 95%CI [1.76, 6.65] for the Boomers I and older group in 2021 and 2022, respectively; and OR = 2.54, 95%CI [1.35, 4.81] for the Generation X group in 2022). No major differences in climate-related risk perceptions were found between the different generation groups in 2021 or 2022, with only a few significant effects, which is in contrast to the results for 2020. Most significantly, there were still generational differences in the strength of climate-related emotions in both 2021 and 2022, in particular between the two baby boomer groups and Generation Z.

Climate-related beliefs, risk perceptions and emotions as repeated measures

The results from the linear and logistic regression analyses suggest that the generational gap is stronger for climate-related risk perceptions and emotions than for climate-related beliefs, and that this gap diminished between 2020 and 2021/2022. To more robustly test generational differences, we conducted a series of consecutive multilevel regression models in which nine out of the ten variables were considered as repeated measures (Level 1) clustered within individuals (Level 2). This approach allows for cross-level interactions between the different generational groups on the one hand and the type of measures (i.e., whether they are about climate-related beliefs, risk perceptions or emotions) on the other.

An empty ‘null’ model, without any predictors, shows that the intraclass correlation (ICC) was 0.39 in 2020. The ICC expresses the fraction of the total variation that can be accounted for by between-person differences rather than within-person differences. This means that 39% of the variance is shared across the nine repeated measures. This shared variance can be attributed to the individual and is likely to reflect a person’s general concern about climate change. Comparable results were found for 2021 (ICC = 0.36) and 2022 (ICC = 0.33).

Results in Table  3 show that, in 2020, the two baby boomer groups expressed less engagement with climate change across the nine repeated measures (Model 1). When the different types of measures were considered in Model 2, it appeared that the generational differences were in climate-related risk perceptions and emotions, and not in climate-related beliefs. In particular, the Boomers II and Boomers I and older groups had lower climate-related risk perceptions and emotions than Generation Z, while Generation X only had lower climate-related emotions than Generation Z.

The results suggest that the generational gap in engagement with climate change across the nine repeated measures did not exist in 2021 and was smaller in 2022 than in 2020 (Model 1). When differences in the three types of measures were considered in Model 2, results for 2021 were largely comparable to those for 2020, in that there are similar generational differences in climate-related risk perceptions and emotions. The study however suggests that the change is only for climate-related beliefs, and not for perceived risks or emotions. Results from 2022 largely followed the same pattern, although generational differences for risk perceptions also disappeared in this period. The differences in emotional engagement with climate change however remained significant across all years. This adds confidence to the finding that differences between generation groups are mainly regarding affective, not cognitive, engagement with climate change.

When combining the three survey years for an overall analysis (see Supplementary Table  1 ), it appears that Generation X and the two baby boomer groups have less engagement with climate change than Generation Z, and that these differences are due to differences in risk perceptions and climate-related emotions. The two baby boomer groups have lower climate related risk perceptions and emotions than Generation Z, while Generation X only has lower climate related emotions. Overall, there were no differences in climate engagement between Millennials and Generation Z.

Using three nationally-representative surveys conducted in 2020 to 2022 in the UK, we conclude that generational differences are mainly found in emotional engagement with climate change and less so with regards to cognitive beliefs about the reality and causes of climate change. More precisely, our results show that younger generations more strongly feel the negative emotions of fear, guilt and outrage as compared to older generations. Generational differences in climate change beliefs and perceived impacts were smaller and appear to have narrowed from 2020 to 2021/2022. A surprising finding is that older generations are more likely to think that we are already feeling the effects of climate change. The findings for risk perceptions were more variable across the three surveys, but in two out of the three years we replicate previous research showing that younger people have higher levels of risk perception as well as worry about climate change than older generations 15 . Overall, the findings show the importance of clearly distinguishing between the different constructs of climate-related beliefs, risk perceptions and emotions and consider them separately when exploring generational patterns.

The results provide further clarity to the literature showing that age is of little relevance for climate change scepticism 30 , but that it is an important factor in threat perceptions, climate change worry and other climate-related emotions 1 , 3 , 16 . This suggests that, while there are only negligible differences in climate-related cognitions, younger age groups show stronger emotional engagement with climate change. Although the current study did not explicitly focus on climate anxiety, one of the clearest differences identified was for the emotion of fear. Fear can be a corrosive emotion and could take a heavy toll on younger generations by affecting their action and wellbeing negatively 4 , although experienced negative emotions may also have more positive, motivational effects 7 . Emotions have been shown to play an important role in human responses to climate change 31 , and can help evoke adaptive coping reactions, including sustainable behaviour 7 , support for climate policies 32 , social support 16 , and climate activism 33 . The greater intensity of emotions, such as outrage, may be one of the reasons as to why younger generations demonstrate high levels of active engagement with the issue of climate change 34 . It is worth noting that the emotions of fear, guilt and outrage are generally experienced less than worry, and that all average scores are below the scale midpoint except some for Millennials (i.e. fear) and Generation Z (i.e. fear and outrage), which are just above the scale midpoint. This suggests that climate-related emotions have not yet reached levels that could lead to maladaptive responses or interfere with the younger generation’s ability to function at this stage 4 , 5 .

Our study further identified some notable differences in generational effects between our samples over the last three years. While similar generational differences were found in regard of climate-related emotion, the overall generational gap appears to have diminished from 2020 to 2021/2022 due to a narrowing of climate-related beliefs and to some extent climate-related risk perceptions. This is in contrast to the received understanding and previous research showing that older age groups have lower agreement with anthropogenic climate change 35 . Older generations even appear to have higher levels of beliefs regarding the temporal proximity of climate change than younger generations. This effect may be explained by the declining remarkability of temperature anomalies. Temperature anomalies are rapidly becoming the new normal and are notably different to people who have experienced previous lower frequencies of extreme weather events 36 . This leads to shifting baselines to which current temperatures and experiences with climate-related events are compared 37 . That is, older age groups are able to compare current temperatures and events with a longer reference period when they were less affected by anthropogenic climate change.

The observed generational gap diminishing from 2020 to 2021/2022 may be due to increased media reporting and attention to the topic 38 . Mass protests by the Fridays for Future and Extinction Rebellion movements, the publication of the IPCC special report on 1.5 °C global warming, and extreme weather events had already pushed climate change higher up the media and public agendas 8 , 39 , 40 , only for attention for the issue to be overwhelmed by the COVID-19 pandemic in 2020 41 . The following year saw a resurgence in media coverage of climate change in the UK, in particular following the publication of the UK Net Zero Strategy and reaching a peak at the time of the COP26 conference in Glasgow 38 .

The contribution of the current research is that it examined generational differences across different types of climate engagement including cognitive and affective dimensions. While previous studies have examined generational differences for specific construct measures, and age is routinely included as a socio-demographic factor in climate perception research, this is the first-time generational differences were examined jointly for climate-related beliefs, risk perceptions and emotions. However, the study is cross-sectional, and it is therefore not possible to determine whether the generational differences in climate-related beliefs, risk perception and emotions are due to developmental or cohort effects 35 . The generational differences may be the result of differences in experiences and conditions the different age groups may have had at key stages of their life or reflect that people’s views develop and change as they grow older.

One of the main findings of the study is that generation gap is most consistent when it comes to affective responses to climate change. The question here is whether younger generations will develop less affective response to climate change as they age in line with the current older generation, or whether the experienced emotions will continue or even strengthen. The increasing frequency and severity of extreme weather events 42 , and the psychological responses they evoke 43 , 44 , suggest that climate anxiety among younger age groups is unlikely to follow the same trajectory as older age groups. Cohort and developmental effects can however only be disentangled with well-designed longitudinal studies, which are currently not available. In addition, it is possible that there are period or era effects where all age groups experience the same events and conditions, but the impacts may differ for each group 16 , 45 , 46 . There are indications that cohort, developmental and period effects all play a role in the patterns of engagement with climate change across the different age groups. People become more politically conservative as they age 47 and develop value and trait patterns that are less conducive to an environmental worldview 48 , 49 , 50 , 51 . The results from this study suggest that events that have taken place in the past few years (such as increased media attention, Fridays for Future and Extinction Rebellion protests, and COP26) have had differential impacts across the different generational groups considered, indicating period effects. Other longitudinal research, using 10-year panel data from New Zealand, shows that that older age cohorts started from a lower level of climate change belief, but that different age cohorts increased their belief level at a similar rate 35 . Milfont et al. (2021) were however only able to conduct the analyses for climate change beliefs. Currently, there are no good quality datasets available that allow similar analyses across the different dimensions of climate engagement that were considered in the current study.

In this paper we used the named generations to explore generational differences in engagement with climate change named generational groups based on the theory of generations. It has to be considered that the different generational groups vary in their time span. The baby boomer generation covers almost two decades (and was therefore divided into two sub-groups), Generation X and Millennials span about 15 years each, and Generation Z only involved up to eight years in this study. The relatively large time span of some of the generational groups may mean that individuals who are born close to the cut off with other generation groups may have more in common with those other groups than individuals who are born in the middle of the cohort. Furthermore, given that not all of Generation Z had turned 18 yet at the time of our surveys, this generational group was relatively small and as a result only had small samples in the three survey years. This may have affected the statistical power to detect differences with other generational groups, such as Millennials. We therefore conducted an additional analysis using similarly sized age cohorts of 10 years (born in 2004–1993, 1992–1983, 1982–1973, 1972–1963, 1962–1953, and 1952- and before). These age cohorts broadly match the Generation Z, Millennials, younger Generation X, older Generation X, Boomers II and Boomers I, respectively. The descriptive results for the different age cohorts are provided in Table Supplementary Table  2 . The results of the multilevel analysis are provided in Supplementary Table  3 . The age cohort analysis validates the results from the generational analysis. There are only minimal differences between the 2004–1993 and 1992–1983 cohorts (roughly matching Generation Z and Millennials, respectively). Generational differences can be found between the 2004–1993 cohort on the one hand and the 1982–1973, 1972–1963, 1962–1953, and 1952 – cohorts on the other. The 1972–1963, 1962–1953, and 1952 – cohorts (roughly matching older Generation X, Boomers II and Boomers I, respectively) have lower climate-related risk perceptions and emotions than the 2004–1993 cohort, while the 1982–1973 cohort (younger Generation Xers) only has lower climate related emotions as compared to the 2004–1993 cohort. This shows that even with other cut-off points for the age groups, the main conclusion still holds that the generation gap is most consistent when it comes to affective responses to climate change, rather than to beliefs about whether climate change exists or is caused by human activity.

Remaining research gaps are about whether similar patterns can be found in other countries and cultures. The named generations used in this study are based on the theory of generations that was developed in a Western context and anchored around events and conditions within the Western world. Similar generational groups and patterns may therefore not apply to different countries or populations. Furthermore, little is known about the consequences of generational differences in engagement with climate change. The implications of climate related emotions for younger generations’ mental and physical wellbeing need to be considered 4 , including how cognitive and emotional engagement can be fostered for constructive and avoiding maladaptive outcomes 6 . Here it is essential to not put the onus on the younger generations to take action. Older generations are in a position of power to shape policies that will help to reduce the risks for future generations. The current study shows that, while there are no generational differences in the acknowledgement of the reality and seriousness of climate change, emotional engagement among older generations appears to be lacking. An important avenue of research is therefore on how communications and interventions can be used to bolster the emotional engagement of older generations for the benefit of the younger and future generations.

The surveys

We used the first three waves of a series of cross-sectional online surveys conducted by the CAST Centre, with data collected between 29th September and 26th October 2020, 28th August and 22nd September 2021, and 5th September and 6th October 2022 by the survey company DJS research. Participants were recruited through online panels. Informed consent was obtained from all participants. The samples were broadly representative of the British population with quotas for gender, age, region, and socioeconomic status. The methodology used to collect the data was consistent across the three waves of the survey. The first wave (2020) of data consisted of 1893 participants, including booster samples in Scotland ( n  = 485) and Wales ( n  = 467). The second wave (2021) of data consisted of 1001 participants. The third wave (2022) included 1087 participants. The surveys obtained approval from the School of Psychology Research Ethics Committee (Wave 1: EC.20.08.11.6068; Wave 2: EC.21.08.10.6385; Wave 3: EC.22.07.12.6597).

The CAST surveys cover a wide range of topics relating to climate change perceptions, policy support and willingness to change behaviours in the areas of food and diet, transport and mobility, household energy use, and material consumption. Here, we specifically focus on the items that were designed to measure climate-related beliefs, risk perceptions and emotions respectively (see Table  1 ).

Climate-related beliefs comprised three items. Perceived causes of climate change was measured with the item “Thinking about the causes of climate change, which, if any, of the following best describes your opinion”. Participants answered the question using a scale that ranged from 1 (Climate change is entirely caused by natural processes) to 5 (Climate change is completely caused by human activity), with 3 representing “Climate change is partly caused by natural processes and partly caused by human activity”. A few responded with “There is no such thing as climate change“ ( n  = 18, n  = 23, and n  = 7 for the three waves respectively), which was coded as 0. Perceived temporal distance was measured with the item “When, if at all, do you think the UK will start feeling the effects of climate change?”. Here, respondents could choose from seven options (We are already feeling the effects; In the next 10 years; In the next 25 years; In the next 50 years; In the next 100 years; Beyond the next 100 years; Never). The distribution of responses warranted a recoding into a dummy variable to compare “We are already feeling the effects” (1) against all other responses (0). 66.8%, 64.6% and 68.4 of the respondents said that we are already feeling the effects of climate change in 2020, 2021, and 2022 respectively, with low numbers for the remaining categories (9.6%, 7.0%, 3.8%, 1.0%, 1.2% and 2.0% for 2020; 11.6%, 8.3%, 3.6%, 0.7%, 1.2% and 1.9% for 2021; and 11.2%, 6.5%, 3.1%, 1.4%, 0.9% and 0.9% for 2022). Respondents indicated their perceived level of urgency in response to the question “Which of these best describes your views about the level of urgency with which climate change needs to be addressed?”. The response scale ranged from 1 (Addressing climate change requires little or no urgency) to 5 (Addressing climate change requires and extremely high level of urgency).

Climate-related risk perceptions consisted of the perceived impacts of climate change (“Overall, how positive or negative do you think the effects of climate change will be on the UK?”), and the perceived threats of climate change to (a) self and family, and (b) to the UK (“How serious a threat, if at all, is climate change to each of the following?“; (a) “… you and your family”, and (b) “…the UK as a whole”). The former could be answered using a bipolar 5-point answer scale anchored by 1 (Entirely positive) and 5 (Entirely negative), and a scale midpoint of 3 (Neither positive nor negative). The latter could be answered using a unipolar 5-point answer scale ranging from 1 (Not serious at all) to 5 (Extremely serious).

Climate-related emotions comprised “worry”, “fear”, “guilt” and “outrage”. Respondents expressed their levels of worry about climate change on a 5-point scale, ranging from 1 (Not at all worried) to 5 (Extremely worried). Respondents were asked to indicate their levels of fear, guilt and outrage in response to the question “When you think about climate change and everything that you associate with it, how strongly, if at all, do you feel each of the following emotions?”. Here respondent could use a scale ranging from 1 (Not at all) to 5 (Very much).

The main independent variable comprised the categorisation of respondents into the seven main generations of Generation Z (born between 1996–2010), Millennials (born between 1981 and 1995), Generation X (born between 1965 and 1980), Boomers II or Generation Jones (born between 1955 and 1964), Boomers I (born between 1946 and 1954) and the Post-war or ‘silent’ generation (born between 1928 and 1945). The survey did not include respondents from the Greatest generation (born between 1901 and 1927). Due to the low numbers for the Post-war generation, these were combined with the Boomers I groups. The study further used the covariates of gender (male and female), education (having a university degree or not) political orientation , and home nation (England, Scotland or Wales). Political orientation was determined using an 11-point self-placement scale ranging from 1 (left) to 11 (right). The scale was standardised by calculating the Z scores across the three waves (Wave 1: M  = 6.29, SD = 2.57; Wave 2: M  = 6.40, SD = 2.22; M  = 6.18, SD = 2.21).

Analytical approach

The cross-sectional analyses consisted of a series of (1) linear and logistic regressions and (2) multilevel analyses. All analyses were conducted using R statistical software (version 4.0.2) in combination with RStudio (version 2021.09.0 + 351) and the stats 52 and lme4 53 packages. The R code can be accessed at https://doi.org/10.17605/OSF.IO/DKRCB .

First, the different climate-related beliefs (perceived causes, already feeling effects, and perceived urgency of climate change), risk perceptions (perceived impacts, perceived threat to self and family, and perceived threat to the UK), and emotions (worry, fear, guilt and outrage) were regressed on the different generational groups, with Generation Z as the reference group. Gender, degree, and political orientation were included as covariates. Linear regression models were constructed, except for ‘already feeling the effects’ for which an ordinal regression model was fitted. Analyses were conducted separately for the three consecutive survey years.

Second, the data were analysed from a multilevel repeated measures perspective 54 . This specific cross-sectional analysis considers the climate-related beliefs, risk perceptions and emotions as repeated measures (Level 1) that are nested within participants (Level 2). In this approach the measures can be conceptualised as repeated judgments about climate change made by (and thus nested within) individuals, with the judgments differing in terms of their content, i.e. they are judgments relating to the reality and nature of climate change (beliefs), the risks and consequences of climate change (risk perceptions), and how climate change is experienced emotionally (emotions) respectively. This approach can be used to apportion variance that is specific to and common across the different measures, and thus allows for the assessment of cross-level interactions between measure-specific (e.g. measure type) and individual-level characteristics (e.g. generational group). Two sets of multilevel models were constructed. The first set (Model 1) included the different generation groups, the covariates as the independent variables (gender, education, and political orientation). The second set (Model 2) added two measure-specific dummy variables identifying the Risk perception and Emotion questions respectively, as well as their interactions with the different generation groups. Generation Z was used as the reference group throughout. In both models, the different climate-related beliefs, risk perceptions and emotions served as the dependent variables. The binary’already feeling the effects’ variable was omitted from the analyses. An empty ‘null’ model (Model 0), without any predictors, was also constructed to estimate the intraclass correlation (ICC), representing the proportion of variance that is common across the different measures and thus can be attributed to the individual (Level 2) rather than to a specific measure (Level 1). The analyses were conducted separately for 2020, 2021, and 2022 data, with a combined analysis provided in Supplementary Table  1 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data and accompanying documents can be accessed at https://doi.org/10.17605/OSF.IO/DKRCB . All data and accompanying documents can be accessed at the UK Data Service ( https://ukdataservice.ac.uk ) after 31 July 2024.

Code availability

The questionnaires and R code can be accessed at https://doi.org/10.17605/OSF.IO/DKRCB .

Ballew, M. et al. Do younger generations care more about global warming . https://climatecommunication.yale.edu/publications/do-younger-generations-care-more-about-global-warming/ (2019).

Milfont, T. L. The interplay between knowledge, perceived efficacy, and concern about global warming and climate change: a one-year longitudinal study. Risk Anal. Off. Publ. Soc. Risk Anal. 32 , 1003–1020 (2012).

Google Scholar  

Poortinga, W., Whitmarsh, L., Steg, L., Böhm, G. & Fisher, S. Climate change perceptions and their individual-level determinants: A cross-European analysis. Glob. Environ. Change 55 , 25–35 (2019).

Article   Google Scholar  

Clayton, S. & Karazsia, B. T. Development and validation of a measure of climate change anxiety. J. Environ. Psychol. 69 , 101434 (2020).

Hickman, C. et al. Climate anxiety in children and young people and their beliefs about government responses to climate change: a global survey. Lancet Planet Health 5 , e863–e873 (2021).

Ojala, M., Cunsolo, A., Ogunbode, C. A. & Middleton, J. Anxiety, worry, and grief in a time of environmental and climate crisis: A narrative review. Annu. Rev. Environ. Resour. 46 , 35–58 (2021).

Whitmarsh, L. et al. Climate anxiety: What predicts it and how is it related to climate action? J. Environ. Psychol. 83 , 101866 (2022).

Marris, E. Why the world is watching young climate activists. Nature 573 , 471–472 (2019).

Article   CAS   Google Scholar  

Pickard, S. Young environmental activists are doing it themselves. Polit. Insight 10 , 4–7 (2019).

Milfont, T. L., Milojev, P., Greaves, L. M. & Sibley, C. G. Socio-structural and psychological foundations of climate change beliefs. N. Z. J. Psychol 44 , 17–30, https://www.psychology.org.nz/journal-archive/Article-21.pdf (2015).

Poortinga, W., Spence, A., Whitmarsh, L., Capstick, S. & Pidgeon, N. F. Uncertain climate: An investigation into public scepticism about anthropogenic climate change. Glob. Environ. Change 21 , 1015–1024 (2011).

Whitmarsh, L. Scepticism and uncertainty about climate change: Dimensions, determinants and change over time. Glob. Environ. Change 21 , 690–700 (2011).

Casey, P. J. & Scott, K. Environmental concern and behaviour in an Australian sample within an ecocentric – anthropocentric framework. Aust. J. Psychol. 58 , 57–67 (2006).

Van Liere, K. D. & Dunlap, R. E. The social bases of environmental concern: A review of hypotheses, explanations and empirical evidence. Public Opin. Q 44 , 181–197 (1980).

Skeirytė, A., Krikštolaitis, R. & Liobikienė, G. The differences of climate change perception, responsibility and climate-friendly behavior among generations and the main determinants of youth’s climate-friendly actions in the EU. J. Environ. Manage. 323 , 116277 (2022).

Swim, J. K., Aviste, R., Lengieza, M. L. & Fasano, C. J. OK Boomer: A decade of generational differences in feelings about climate change. Glob. Environ. Change 73 , 102479 (2022).

Gray, S. G., Raimi, K. T., Wilson, R. & Árvai, J. Will Millennials save the world? The effect of age and generational differences on environmental concern. J. Environ. Manage. 242 , 394–402 (2019).

Wiernik, B., Ones, D. & Dilchert, S. Age and environmental sustainability: A meta-analysis. J. Manag. Psychol. 28 , 826–856 (2013).

Van der Linden, S. Determinants and measurement of climate change risk perception, worry and concern. in Oxford Research Encyclopedia of Climate Science https://doi.org/10.1093/acrefore/9780190228620.013.318 (2017).

Bouman, T. et al. When worry about climate change leads to climate action: How values, worry and personal responsibility relate to various climate actions. Glob. Environ. Change 62 , 1–11 (2020).

Öhman, A. Fear and anxiety: Overlaps and dissociations. in Handbook of emotions , 3rd ed. 709–728 (The Guilford Press, 2008).

Smith, N. & Leiserowitz, A. The role of emotion in global warming policy support and opposition. Risk Anal 34 , 937–948 (2014).

Misslin, R. The defense system of fear: Behavior and neurocircuitry. Neurophysiol. Clin. Clin. Neurophysiol. 33 , 55–66 (2003).

Clayton, S. Climate anxiety: Psychological responses to climate change. J. Anxiety Disord. 74 , 102263 (2020).

Eyerman, R. & Turner, B. S. Outline of a theory of generations. Eur. J. Soc. Theory 1 , 91–106 (1998).

Pilcher, J. Mannheim’s sociology of generations: An undervalued legacy. Br. J. Sociol. 45 , 481–495 (1994).

Wey Smola, K. & Sutton, C. D. Generational differences: Revisiting generational work values for the new millennium. J. Organ. Behav. 23 , 363–382 (2002).

Loria, K. Past generations created a climate crisis for Millennials and Generation Z. Today marks 30 years of inaction ; https://www.businessinsider.com/baby-boomers-millennials-climate-change-generations-2017-7 (Business Insider, 23 June 2017, 2017).

Lashbrook, A. ‘No point in anything else’: Gen Z members flock to climate careers ; https://www.theguardian.com/environment/2021/sep/06/gen-z-climate-change-careers-jobs (The Guardian, 6 September 2021, 2021).

Hornsey, M. J., Harris, E. A., Bain, P. G. & Fielding, K. S. Meta-analyses of the determinants and outcomes of belief in climate change. Nat. Clim. Change 6 , 622–626 (2016).

Brosch, T. & Steg, L. Leveraging emotion for sustainable action. One Earth 4 , 1693–1703 (2021).

Wang, S., Leviston, Z., Hurlstone, M., Lawrence, C. & Walker, I. Emotions predict policy support: Why it matters how people feel about climate change. Glob. Environ. Change 50 , 25–40 (2018).

Kleres, J. & Wettergren, Å. Fear, hope, anger, and guilt in climate activism. Soc. Mov. Stud. 16 , 507–519 (2017).

Tyson, A., Kennedy, B. & Funk, C. Gen, Z. millennials stand out for climate change activism, Social Media Engagement With Issue ; https://www.pewresearch.org/science/2021/05/26/gen-z-millennials-stand-out-for-climate-change-activism-social-media-engagement-with-issue (Pew Research Centre, 2021).

Milfont, T. L., Zubielevitch, E., Milojev, P. & Sibley, C. G. Ten-year panel data confirm generation gap but climate beliefs increase at similar rates across ages. Nat. Commun. 12 , 4038 (2021).

Moore, F. C., Obradovich, N., Lehner, F. & Baylis, P. Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change. Proc. Natl. Acad. Sci. 116 , 4905–4910 (2019).

Papworth, S. K., Rist, J., Coad, L. & Milner-Gulland, E. J. Evidence for shifting baseline syndrome in conservation. Conserv. Lett. 2 , 93–100 (2009).

Oonk, D., Nacu-Smith, A., Chandler, P. & Boykoff, M. United Kingdom Newspaper Coverage of Climate Change or Global Warming, 2000-2022 - June 2022; https://doi.org/10.25810/8196-e820.51 (2022).

Hoffmann, R., Muttarak, R., Peisker, J. & Stanig, P. Climate change experiences raise environmental concerns and promote Green voting. Nat. Clim. Change 12 , 148–155 (2022).

Ogunbode, C. A., Doran, R. & Böhm, G. Exposure to the IPCC special report on 1.5 °C global warming is linked to perceived threat and increased concern about climate change. Clim. Change 158 , 361–375 (2020).

Pearman, O. et al. COVID-19 media coverage decreasing despite deepening crisis. Lancet Planet. Health 5 , e6–e7 (2021).

Coronese, M., Lamperti, F., Keller, K., Chiaromonte, F. & Roventini, A. Evidence for sharp increase in the economic damages of extreme natural disasters. Proc. Natl. Acad. Sci. 116 , 21450–21455 (2019).

Ogunbode, C. A. et al. The resilience paradox: flooding experience, coping and climate change mitigation intentions. Clim. Policy 19 , 703–715 (2019).

Bergquist, M., Nilsson, A. & Schultz, P. Experiencing a severe weather event increases concern about climate change. Front. Psychol . 220, https://doi.org/10.3389/fpsyg.2019.00220 (2019).

Gifford, R. & Nilsson, A. Personal and social factors that influence pro-environmental concern and behaviour: A review. Int. J. Psychol. 49 , 141–157 (2014).

Honnold, J. A. Age and environmental concern some specification of effects. J. Environ. Educ. 16 , 4–9 (1984).

Cornelis, I., Van Hiel, A., Roets, A. & Kossowska, M. Age differences in conservatism: Evidence on the mediating effects of personality and cognitive style. J. Pers. 77 , 51–88 (2009).

Brandt, N. D. et al. Acting like a baby boomer? Birth-cohort differences in adults’ personality trajectories during the last half a century. Psychol. Sci. 33 , 382–396 (2022).

Geiger, N., McLaughlin, B. & Velez, J. Not all boomers: Temporal orientation explains inter- and intra-cultural variability in the link between age and climate engagement. Clim. Change 166 , 12 (2021).

Milojev, P. & Sibley, C. G. Normative personality trait development in adulthood: A 6-year cohort-sequential growth model. J. Pers. Soc. Psychol. 112 , 510–526 (2017).

Zubielevitch, E., Osborne, D., Milojev, P. & Sibley, C. G. Social dominance orientation and right-wing authoritarianism across the adult lifespan: An examination of aging and cohort effects. J. Pers. Soc. Psychol. 124 , 544–566 (2022).

R. Core Team. R: A language and environment for statistical computing ; http://www.R-project.org (R Core Team, 2021).

Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J Stat. Softw. 67 , 1–48 (2015).

Poortinga, W., Latter, B. & Wang, S. Comparing coronavirus (COVID-19) and climate change perceptions: Implications for support for individual and collective-level policies. Front. Psychol . 13 , https://doi.org/10.3389/fpsyg.2022.996546 (2022).

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We acknowledge support from the Economic & Social Research Council (ESRC) through the Centre for Climate Change and Social Transformations (CAST), Grant Ref: ES/S012257/1.

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Poortinga, W., Demski, C. & Steentjes, K. Generational differences in climate-related beliefs, risk perceptions and emotions in the UK. Commun Earth Environ 4 , 229 (2023). https://doi.org/10.1038/s43247-023-00870-x

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Home — Essay Samples — Sociology — Generation Gap — Generation Gap: The Differences Between New Generation And Parents’ Generation

Generation Gap: How Today's Generation is Different from Their Parents' Generation

• Categories: Generation Generation Gap

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Published: Mar 18, 2021

Words: 1119 | Pages: 2 | 6 min read

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Introduction, difference between old and new generation, works cited:, jobs fields, educational prospects.

• Bergsteiner, H., & Avery, G. C. (2010). The importance of learning styles: Understanding the implications for learning, course design, and education. Education Sciences, 1(2), 116-125.
• Kinsella, E. A. (2010). The Kolb Learning Style Inventory—Version 3.1 2005 technical specifications. The Irish Journal of Psychology, 31(2-3), 271-276.
• McDonald, J., & Boud, D. (2003). The impact of self-assessment on achievement: The effects of self-assessment training on performance in external examinations. Assessment in Education: Principles, Policy & Practice, 10(2), 209-220.
• Murphy, J. (2004). Exploring Kolb's learning styles in management education. Journal of Management Education, 28(2), 191-210.
• Nicol, D., Macfarlane‐Dick, D., & Macfarlane, S. (2006). Rethinking feedback practices in higher education: a peer review perspective. Assessment & Evaluation in Higher Education, 31(2), 139-149

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Bridging Generational Divides in Your Workplace

by Debra Sabatini Hennelly and Bradley Schurman

Summary .   

Due largely to early retirements and a caustic mix of ageism and cost-cutting measures, businesses let too many older workers go during the pandemic — and when they left, so did a lot of institutional memory, expertise, and loyalty. With fewer younger workers entering the labor market for at least a generation, employers that don’t think beyond today’s working-age population will likely struggle to build a reliable workforce that can maintain operational efficiency and effectiveness. They must reconsider their DEI strategies to meet the demands of a new era if they want to drive operational effectiveness, increase competitiveness, widen their appeal to consumers of all ages and abilities, and build long-term resilience. The authors describe how leaders can account for the changes — and benefits — that come with an aging workforce to power productivity into the future.

Demographic change is one of the least understood yet profoundly important issues facing organizations today. The “working-age population” in the U.S. — those from age 16 to 64 — is contracting at a pace not experienced since World War II. Unlike that period, there is no “baby boom” behind it, and none is expected in the near future. Generation Z has three million fewer people than the Millennial generation, and Generation Alpha, which follows Gen Z, is expected to be even smaller.

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How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

• Age & Generations
• Demographic Research
• Generation X
• Generation Z
• Generations
• Greatest Generation
• Methodological Research
• Millennials
• Silent Generation

Kim Parker is director of social trends research at Pew Research Center .

U.S. adults under 30 have different foreign policy priorities than older adults

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When navigating their health journey, patients have an overwhelming number of options for finding medical information and making purchases. Marketers must tailor their engagement strategies via a unique mix of digital and traditional channels to best reach each patient generation.

Key Question: Where do US consumers typically begin their online research for medical information?

Key Stat: Gen Zers and millennials increasingly start their patient journeys on social media platforms, while Gen Xers and baby boomers are more likely to turn to search engines and medical information websites, according to our December 2023 US Digital Health survey.

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As Groups Have Shifted, Has It Become a ‘Normal’ Election?

Unusual demographic patterns are fading, but there are still some differences from the 2020 race.

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By Nate Cohn

Labor Day usually marks the start of the heart of campaign season, but this year it felt like a lull — a brief respite after two tumultuous months.

At the end of it all, the presidential campaign almost feels as if it’s back to “normal.” The candidates fought over the issues and their agendas . There were no questions about whether a candidate was going to drop out. And if the polls are any indication, public opinion is finally settling into something more like normal as well.

Just take a look at our polling averages , which have been updated since Robert F. Kennedy Jr.’s departure from the race (a return to normal in its own right). There’s no sign of the political chaos of the last few months. Instead, the results look typical: Nationwide, Kamala Harris leads Donald J. Trump by three percentage points, 49 percent to 46 percent. Across the battleground states, the race is a dead heat. In every state and nationwide, the polling average is within 1.5 points of the result of the 2020 presidential election.

In short, the polls finally show the close election that analysts expected a year ago, before President Biden’s candidacy went off the rails. If anything, it’s even closer than expected: The polling averages today are closer than the final pre-election polling in any presidential election in the era of modern polling — closer than 2000, 2004 or 2012, let alone 2016 or 2020.

The uncommon demographic patterns of the last year — the erosion of support for Mr. Biden among traditionally Democratic groups — have been fading as well. But here there are a few more vestiges of what we saw in the unusual Biden-Trump polling. In some cases, it’s a bit of a surprise. Here’s how the race is — or isn’t — returning to normal.

The return of the generational divide

The Democratic lead among young voters is back. In high-quality polls over the last month, Vice President Harris leads Mr. Trump by an average of 20 points among the youngest reported demographic cohort (whether that be 18 to 29 or 18 to 34 in a given poll). The same polls showed Mr. Biden and Mr. Trump tied among young voters in July. Older voters, meanwhile, have barely edged at all toward Ms. Harris. Put it together, and the usual generational divide in American politics has returned.

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